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127 results on '"Oreiller, Valentin"'

1. A Bispectral 3D U-Net for Rotation Robustness in Medical Segmentation

Author

Chevalley, Arthur, Oreiller, Valentin, Fageot, Julien, Prior, John O., Andrearczyk, Vincent, Depeursinge, Adrien, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chen, Chao, editor, Singh, Yash, editor, and Hu, Xiaoling, editor

Published
2025
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2. Why is the winner the best?

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Ali, Sharib, Andrearczyk, Vincent, Aubreville, Marc, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Cheplygina, Veronika, Daum, Marie, de Bruijne, Marleen, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Ellis, David G., Engelhardt, Sandy, Ganz, Melanie, Ghatwary, Noha, Girard, Gabriel, Godau, Patrick, Gupta, Anubha, Hansen, Lasse, Harada, Kanako, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Jannin, Pierre, Kavur, Ali Emre, Kodym, Oldřich, Kozubek, Michal, Li, Jianning, Li, Hongwei, Ma, Jun, Martín-Isla, Carlos, Menze, Bjoern, Noble, Alison, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Rädsch, Tim, Rafael-Patiño, Jonathan, Bawa, Vivek Singh, Speidel, Stefanie, Sudre, Carole H., van Wijnen, Kimberlin, Wagner, Martin, Wei, Donglai, Yamlahi, Amine, Yap, Moi Hoon, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Aydogan, Dogu Baran, Bhattarai, Binod, Bloch, Louise, Brüngel, Raphael, Cho, Jihoon, Choi, Chanyeol, Dou, Qi, Ezhov, Ivan, Friedrich, Christoph M., Fuller, Clifton, Gaire, Rebati Raman, Galdran, Adrian, Faura, Álvaro García, Grammatikopoulou, Maria, Hong, SeulGi, Jahanifar, Mostafa, Jang, Ikbeom, Kadkhodamohammadi, Abdolrahim, Kang, Inha, Kofler, Florian, Kondo, Satoshi, Kuijf, Hugo, Li, Mingxing, Luu, Minh Huan, Martinčič, Tomaž, Morais, Pedro, Naser, Mohamed A., Oliveira, Bruno, Owen, David, Pang, Subeen, Park, Jinah, Park, Sung-Hong, Płotka, Szymon, Puybareau, Elodie, Rajpoot, Nasir, Ryu, Kanghyun, Saeed, Numan, Shephard, Adam, Shi, Pengcheng, Štepec, Dejan, Subedi, Ronast, Tochon, Guillaume, Torres, Helena R., Urien, Helene, Vilaça, João L., Wahid, Kareem Abdul, Wang, Haojie, Wang, Jiacheng, Wang, Liansheng, Wang, Xiyue, Wiestler, Benedikt, Wodzinski, Marek, Xia, Fangfang, Xie, Juanying, Xiong, Zhiwei, Yang, Sen, Yang, Yanwu, Zhao, Zixuan, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work., Comment: accepted to CVPR 2023

Published
2023

3. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022

4. Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Author

Andrearczyk, Vincent, Oreiller, Valentin, Boughdad, Sarah, Rest, Catherine Chez Le, Elhalawani, Hesham, Jreige, Mario, Prior, John O., Vallières, Martin, Visvikis, Dimitris, Hatt, Mathieu, and Depeursinge, Adrien

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects.

Published
2022

5. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Author

Andrearczyk, Vincent, Oreiller, Valentin, Abobakr, Moamen, Akhavanallaf, Azadeh, Balermpas, Panagiotis, Boughdad, Sarah, Capriotti, Leo, Castelli, Joel, Cheze Le Rest, Catherine, Decazes, Pierre, Correia, Ricardo, El-Habashy, Dina, Elhalawani, Hesham, Fuller, Clifton D., Jreige, Mario, Khamis, Yomna, La Greca, Agustina, Mohamed, Abdallah, Naser, Mohamed, Prior, John O., Ruan, Su, Tanadini-Lang, Stephanie, Tankyevych, Olena, Salimi, Yazdan, Vallières, Martin, Vera, Pierre, Visvikis, Dimitris, Wahid, Kareem, Zaidi, Habib, Hatt, Mathieu, Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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7. 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis

Author

Oreiller, Valentin, Andrearczyk, Vincent, Fageot, Julien, Prior, John O., and Depeursinge, Adrien

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis where patterns appear at random positions and orientations. LRI operators can be obtained by computing the responses to the discrete rotation of local descriptors, such as Local Binary Patterns (LBP) or the Scale Invariant Feature Transform (SIFT). Other strategies achieve this invariance using Laplacian of Gaussian or steerable wavelets for instance, preventing the introduction of sampling errors during the discretization of the rotations. In this work, we obtain LRI operators via the local projection of the image on the spherical harmonics basis, followed by the computation of the bispectrum, which shares and extends the invariance properties of the spectrum. We investigate the benefits of using the bispectrum over the spectrum in the design of a LRI layer embedded in a shallow Convolutional Neural Network (CNN) for 3D image analysis. The performance of each design is evaluated on two datasets and compared against a standard 3D CNN. The first dataset is made of 3D volumes composed of synthetically generated rotated patterns, while the second contains malignant and benign pulmonary nodules in Computed Tomography (CT) images. The results indicate that bispectrum CNNs allows for a significantly better characterization of 3D textures than both the spectral and standard CNN. In addition, it can efficiently learn with fewer training examples and trainable parameters when compared to a standard convolutional layer., Comment: 10 pages

Published
2020

8. Local Rotation Invariance in 3D CNNs

Author

Andrearczyk, Vincent, Fageot, Julien, Oreiller, Valentin, Montet, Xavier, and Depeursinge, Adrien

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations. LRI constituted the cornerstone of several breakthroughs in texture analysis, including Local Binary Patterns (LBP), Maximum Response 8 (MR8) and steerable filterbanks. Whereas globally rotation invariant Convolutional Neural Networks (CNN) were recently proposed, LRI was very little investigated in the context of deep learning. LRI designs allow learning filters accounting for all orientations, which enables a drastic reduction of trainable parameters and training data when compared to standard 3D CNNs. In this paper, we propose and compare several methods to obtain LRI CNNs with directional sensitivity. Two methods use orientation channels (responses to rotated kernels), either by explicitly rotating the kernels or using steerable filters. These orientation channels constitute a locally rotation equivariant representation of the data. Local pooling across orientations yields LRI image analysis. Steerable filters are used to achieve a fine and efficient sampling of 3D rotations as well as a reduction of trainable parameters and operations, thanks to a parametric representations involving solid Spherical Harmonics (SH), which are products of SH with associated learned radial profiles.Finally, we investigate a third strategy to obtain LRI based on rotational invariants calculated from responses to a learned set of solid SHs. The proposed methods are evaluated and compared to standard CNNs on 3D datasets including synthetic textured volumes composed of rotated patterns, and pulmonary nodule classification in CT. The results show the importance of LRI image analysis while resulting in a drastic reduction of trainable parameters, outperforming standard 3D CNNs trained with data augmentation.

Published
2020

10. Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT Prediction

Author

Abler, Daniel, Andrearczyk, Vincent, Oreiller, Valentin, Garcia, Javier Barranco, Vuong, Diem, Tanadini-Lang, Stephanie, Guckenberger, Matthias, Reyes, Mauricio, Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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12. Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Author

Andrearczyk, Vincent, Oreiller, Valentin, Jreige, Mario, Vallières, Martin, Castelli, Joel, Elhalawani, Hesham, Boughdad, Sarah, Prior, John O., Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, and Depeursinge, Adrien, editor

Published
2021
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15. Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT

Author

Fontaine, Pierre, Andrearczyk, Vincent, Oreiller, Valentin, Castelli, Joël, Jreige, Mario, Prior, John O., Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Syeda-Mahmood, Tanveer, editor, Li, Xiang, editor, Madabhushi, Anant, editor, Greenspan, Hayit, editor, Li, Quanzheng, editor, Leahy, Richard, editor, Dong, Bin, editor, and Wang, Hongzhi, editor

Published
2021
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16. Multi-task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer

Author

Andrearczyk, Vincent, Fontaine, Pierre, Oreiller, Valentin, Castelli, Joel, Jreige, Mario, Prior, John O., Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rekik, Islem, editor, Adeli, Ehsan, editor, Park, Sang Hyun, editor, and Schnabel, Julia, editor

Published
2021
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17. Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Author

Andrearczyk, Vincent, primary, Oreiller, Valentin, additional, Boughdad, Sarah, additional, Rest, Catherine Cheze Le, additional, Elhalawani, Hesham, additional, Jreige, Mario, additional, Prior, John O., additional, Vallières, Martin, additional, Visvikis, Dimitris, additional, Hatt, Mathieu, additional, and Depeursinge, Adrien, additional

Published
2022
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22. QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

Author

Abler, Daniel (author), Schaer, Roger (author), Oreiller, Valentin (author), Verma, H. (author), Reichenbach, Julien (author), Aidonopoulos, Orfeas (author), Evéquoz, Florian (author), Jreige, Mario (author), Prior, John (author), Abler, Daniel (author), Schaer, Roger (author), Oreiller, Valentin (author), Verma, H. (author), Reichenbach, Julien (author), Aidonopoulos, Orfeas (author), Evéquoz, Florian (author), Jreige, Mario (author), and Prior, John (author)

Abstract

Background: Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. Methods: We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. Results: Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports “no-code” development and evaluation of machine learning models against patient-specific outcome data. Conclusions: QI2 fills a gap in the radiomics software landscape by enabling “no-code” radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/. Key points As domain experts, physicians play a key role in the, Human-Centred Artificial Intelligence

Published
2023
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24. Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Author

Ma, Baoqiang, Li, Yan, Chu, Hung, Tang, Wei, De la O Arévalo, Luis Ricardo, Guo, Jiapan, van Ooijen, Peter, Both, Stefan, Langendijk, Johannes Albertus, van Dijk, Lisanne V., Sijtsema, Nanna Maria, Andrearczyk, Vincent, Oreiller, Valentin, Depeursinge, Adrien, Hatt, Mathieu, Intelligent Systems, Damage and Repair in Cancer Development and Cancer Treatment (DARE), and Guided Treatment in Optimal Selected Cancer Patients (GUTS)

Subjects
Oropharyngeal cancer, Radiomics, Deep learning, Outcome prediction, Head and neck cancer, Recurrence-free survival
Abstract

Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.

Published
2023

26. Validating and optimizing the specificity of imaging biomarkers for personalized medicine

Author

Oreiller, Valentin and Oreiller, Valentin

Abstract

In the last decade, biomedical image analysis has advanced significantly, thanks to radiomics and Convolutional Neural Networks (CNNs) being used for constructing predictive models in personalized medicine. These models are built using image features that can be divided into three main categories: intensity, shape, and texture. Although intensity and shape features are essential, texture features have the potential to reveal complex relationships between tumor architecture and patient outcomes. Therefore, the advancement and comprehension of texture features hold potential as effective strategies for clinicians to enhance disease characterization and facilitate personalized medicine. However, common texture features suffer from several limitations. In this thesis, we reviewed the most common texture features, explaining their advantages and disadvantages, and focusing on their robustness to rotations of the images and structures of interest. We proposed a novel method for designing directional image operators that are Locally Rotation Invariant (LRI), which we implemented based on the power spectrum and bispectrum of the circular harmonics expansion for 2D images or the spherical harmonics expansion for 3D images. We further integrated these LRI operators into a convolutional layer and used them in a CNN to obtain various LRI CNNs. We tested several shallow 3D LRI CNNs to classify benign or malignant lung nodules and demonstrated the advantages of bispectral LRI CNNs in terms of accuracy and data efficiency. Additionally, we evaluated our bispectral LRI layer in a 2D U-Net to segment nuclei in histopathological images and obtained comparable performance between the LRI U-Net and a standard U-Net. Furthermore, we showed that the LRI U-Net was more resilient to input rotations than the standard U-Net. The development of machine learning in biomedical imaging requires large datasets and benchmarks to create robust predictive models. Therefore, the second contribution o

Published
2022

27. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

Author

Oreiller, Valentin, primary, Andrearczyk, Vincent, additional, Jreige, Mario, additional, Boughdad, Sarah, additional, Elhalawani, Hesham, additional, Castelli, Joel, additional, Vallières, Martin, additional, Zhu, Simeng, additional, Xie, Juanying, additional, Peng, Ying, additional, Iantsen, Andrei, additional, Hatt, Mathieu, additional, Yuan, Yading, additional, Ma, Jun, additional, Yang, Xiaoping, additional, Rao, Chinmay, additional, Pai, Suraj, additional, Ghimire, Kanchan, additional, Feng, Xue, additional, Naser, Mohamed A., additional, Fuller, Clifton D., additional, Yousefirizi, Fereshteh, additional, Rahmim, Arman, additional, Chen, Huai, additional, Wang, Lisheng, additional, Prior, John O., additional, and Depeursinge, Adrien, additional

Published
2022
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28. Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

Author

Ma, Baoqiang, Guo, Jiapan, De Biase, Alessia, Sourlos, Nikos, Tang, Wei, van Ooijen, Peter, Both, Stefan, Sijtsema, Nanna Maria, Andrearczyk, Vincent, Oreiller, Valentin, Hatt, Mathieu, Depeursinge, Adrien, Intelligent Systems, ​Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), and Damage and Repair in Cancer Development and Cancer Treatment (DARE)

Subjects
Progression free survival prediction, OPSCC, Autoencoder, DeepSurv, PET-scans and CT-scans
Abstract

Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high- or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. The proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. The whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. The C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints.

Published
2022

29. Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images

Author

De Biase, Alessia, Tang, Wei, Sourlos, Nikos, Ma, Baoqiang, Guo, Jiapan, Sijtsema, Nanna Maria, van Ooijen, Peter, Andrearczyk, Vincent, Oreiller, Valentin, Hatt, Mathieu, Depeursinge, Adrien, Intelligent Systems, ​Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), and Damage and Repair in Cancer Development and Cancer Treatment (DARE)

Subjects
Head and neck tumor segmentation, PET-CT, Radiotherapy treatment planning, Squeeze-and-excitation, Oropharynx, Deep learning, HECKTOR2021, Normalization, Co-learning, AI, Multi-modal, Automatic segmentation, Head and neck cancer, Neural networks
Abstract

One of the primary treatment options for head and neck cancer is (chemo)radiation. Accurate delineation of the contour of the tumors is of great importance in the successful treatment of the tumor and in the prediction of patient outcomes. With this paper we take part in the HECKTOR 2021 challenge and we propose our methods for automatic tumor segmentation on PET and CT images of oropharyngeal cancer patients. To achieve this goal, we investigated different deep learning methods with the purpose of highlighting relevant image and modality related features, to refine the contour of the primary tumor. More specifically, we tested a Co-learning method [1] and a 3D Skip Spatial and Channel Squeeze and Excitation Multi-Scale Attention method (Skip-scSE-M), on the challenge dataset. The best results achieved on the test set were 0.762 mean Dice Similarity Score and 3.143 median of the Hausdorf Distance at 95 %.

Published
2022

30. Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT.

Author

Faist, Daphné, Jreige, Mario, Oreiller, Valentin, Nicod Lalonde, Marie, Schaefer, Niklaus, Depeursinge, Adrien, and Prior, John O.

Subjects
RADIOMICS, LUNG cancer, POSITRON emission tomography, ROOT-mean-squares, PULMONARY nodules, CORRECTION factors
Abstract

Background: Quality and reproducibility of radiomics studies are essential requirements for the standardisation of radiomics models. As recent data-driven respiratory gating (DDG) [18F]-FDG has shown superior diagnostic performance in lung cancer, we evaluated the impact of DDG on the reproducibility of radiomics features derived from [18F]-FDG PET/CT in comparison to free-breathing flow (FB) imaging. Methods: Twenty four lung nodules from 20 patients were delineated. Radiomics features were derived on FB flow PET/CT and on the corresponding DDG reconstruction using the QuantImage v2 platform. Lin's concordance factor (Cb) and the mean difference percentage (DIFF%) were calculated for each radiomics feature using the delineated nodules which were also classified by anatomical localisation and volume. Non-reproducible radiomics features were defined as having a bias correction factor Cb < 0.8 and/or a mean difference percentage DIFF% > 10. Results: In total 141 features were computed on each concordance analysis, 10 of which were non-reproducible on all pulmonary lesions. Those were first-order features from Laplacian of Gaussian (LoG)-filtered images (sigma = 1 mm): Energy, Kurtosis, Minimum, Range, Root Mean Squared, Skewness and Variance; Texture features from Gray Level Cooccurence Matrix (GLCM): Cluster Prominence and Difference Variance; First-order Standardised Uptake Value (SUV) feature: Kurtosis. Pulmonary lesions located in the superior lobes had only stable radiomics features, the ones from the lower parts had 25 non-reproducible radiomics features. Pulmonary lesions with a greater size (defined as long axis length > median) showed a higher reproducibility (9 non-reproducible features) than smaller ones (20 non-reproducible features). Conclusion: Calculated on all pulmonary lesions, 131 out of 141 radiomics features can be used interchangeably between DDG and FB PET/CT acquisitions. Radiomics features derived from pulmonary lesions located inferior to the superior lobes are subject to greater variability as well as pulmonary lesions of smaller size. [ABSTRACT FROM AUTHOR]

Published
2022
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32. Fusion-Based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques

Author

Rezaeijo, Seyed Masoud, Harimi, Ali, Salmanpour, Mohammad R., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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33. Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images

Author

Lyu, Qing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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34. Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients

Author

Juanco-Müller, Ángel Víctor, Mota, João F. C., Goatman, Keith, Hoogendoorn, Corné, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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35. MLC at HECKTOR 2022: The Effect and Importance of Training Data When Analyzing Cases of Head and Neck Tumors Using Machine Learning

Author

Thambawita, Vajira, Storås, Andrea M., Hicks, Steven A., Halvorsen, Pål, Riegler, Michael A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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36. A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer

Author

La Greca Saint-Esteven, Agustina, Motisi, Laura, Balermpas, Panagiotis, Tanadini-Lang, Stephanie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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37. Recurrence-Free Survival Prediction Under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers

Author

Wang, Kai, Li, Yunxiang, Dohopolski, Michael, Peng, Tao, Lu, Weiguo, Zhang, You, Wang, Jing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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38. Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation

Author

Shi, Yaying, Zhang, Xiaodong, Yan, Yonghong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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39. Octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation Refinement

Author

Wang, Anthony, Bai, Ti, Nguyen, Dan, Jiang, Steve, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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40. Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT Scans

Author

Jain, Arnav, Huang, Julia, Ravipati, Yashwanth, Cain, Gregory, Boyd, Aidan, Ye, Zezhong, Kann, Benjamin H., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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41. Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report

Author

Myronenko, Andriy, Siddiquee, Md Mahfuzur Rahman, Yang, Dong, He, Yufan, Xu, Daguang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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42. A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images

Author

Jiang, Hao, Haimerl, Jason, Gu, Xuejun, Lu, Weiguo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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43. A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images

Author

Sun, Xiao, An, Chengyang, Wang, Lisheng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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44. HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT Images

Author

Salahuddin, Zohaib, Chen, Yi, Zhong, Xian, Rad, Nastaran Mohammadian, Woodruff, Henry C., Lambin, Philippe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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45. Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images

Author

Wang, Yiling, Lombardo, Elia, Huang, Lili, Belka, Claus, Riboldi, Marco, Kurz, Christopher, Landry, Guillaume, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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46. Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer

Author

Salmanpour, Mohammad R., Hajianfar, Ghasem, Hosseinzadeh, Mahdi, Rezaeijo, Seyed Masoud, Hosseini, Mohammad Mehdi, Kalatehjari, Ehsanhosein, Harimi, Ali, Rahmim, Arman, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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47. Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network

Author

Chen, Jianan, Martel, Anne L., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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48. Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images

Author

Xu, Hui, Li, Yihao, Zhao, Wei, Quellec, Gwenolé, Lu, Lijun, Hatt, Mathieu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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49. Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation

Author

Srivastava, Abhishek, Jha, Debesh, Aydogan, Bulent, Abazeed, Mohamed E., Bagci, Ulas, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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50. Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer

Author

Meng, Mingyuan, Bi, Lei, Feng, Dagan, Kim, Jinman, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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