Search

Your search keyword '"Di Ieva, Antonio"' showing total 674 results
Start Over Author "Di Ieva, Antonio"
674 results on '"Di Ieva, Antonio"'

1. Diagnostic Performance of Deep Learning for Predicting Gliomas' IDH and 1p/19q Status in MRI: A Systematic Review and Meta-Analysis

Author

Farahani, Somayeh, Hejazi, Marjaneh, Tabassum, Mehnaz, Di Ieva, Antonio, Mahdavifar, Neda, and Liu, Sidong

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Gliomas, the most common primary brain tumors, show high heterogeneity in histological and molecular characteristics. Accurate molecular profiling, like isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion, is critical for diagnosis, treatment, and prognosis. This review evaluates MRI-based deep learning (DL) models' efficacy in predicting these biomarkers. Following PRISMA guidelines, we systematically searched major databases (PubMed, Scopus, Ovid, and Web of Science) up to February 2024, screening studies that utilized DL to predict IDH and 1p/19q codeletion status from MRI data of glioma patients. We assessed the quality and risk of bias using the radiomics quality score and QUADAS-2 tool. Our meta-analysis used a bivariate model to compute pooled sensitivity, specificity, and meta-regression to assess inter-study heterogeneity. Of the 565 articles, 57 were selected for qualitative synthesis, and 52 underwent meta-analysis. The pooled estimates showed high diagnostic performance, with validation sensitivity, specificity, and area under the curve (AUC) of 0.84 [prediction interval (PI): 0.67-0.93, I2=51.10%, p < 0.05], 0.87 [PI: 0.49-0.98, I2=82.30%, p < 0.05], and 0.89 for IDH prediction, and 0.76 [PI: 0.28-0.96, I2=77.60%, p < 0.05], 0.85 [PI: 0.49-0.97, I2=80.30%, p < 0.05], and 0.90 for 1p/19q prediction, respectively. Meta-regression analyses revealed significant heterogeneity influenced by glioma grade, data source, inclusion of non-radiomics data, MRI sequences, segmentation and feature extraction methods, and validation techniques. DL models demonstrate strong potential in predicting molecular biomarkers from MRI scans, with significant variability influenced by technical and clinical factors. Thorough external validation is necessary to increase clinical utility.

Published
2024

2. When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis

Author

Moradizeyveh, Sahar, Tabassum, Mehnaz, Liu, Sidong, Newport, Robert Ahadizad, Beheshti, Amin, and Di Ieva, Antonio

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

Eye-gaze tracking research offers significant promise in enhancing various healthcare-related tasks, above all in medical image analysis and interpretation. Eye tracking, a technology that monitors and records the movement of the eyes, provides valuable insights into human visual attention patterns. This technology can transform how healthcare professionals and medical specialists engage with and analyze diagnostic images, offering a more insightful and efficient approach to medical diagnostics. Hence, extracting meaningful features and insights from medical images by leveraging eye-gaze data improves our understanding of how radiologists and other medical experts monitor, interpret, and understand images for diagnostic purposes. Eye-tracking data, with intricate human visual attention patterns embedded, provides a bridge to integrating artificial intelligence (AI) development and human cognition. This integration allows novel methods to incorporate domain knowledge into machine learning (ML) and deep learning (DL) approaches to enhance their alignment with human-like perception and decision-making. Moreover, extensive collections of eye-tracking data have also enabled novel ML/DL methods to analyze human visual patterns, paving the way to a better understanding of human vision, attention, and cognition. This systematic review investigates eye-gaze tracking applications and methodologies for enhancing ML/DL algorithms for medical image analysis in depth.

Published
2024

3. Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

Author

Gao, Yang, Xiao, Xiong, Han, Bangcheng, Li, Guilin, Ning, Xiaolin, Wang, Defeng, Cai, Weidong, Kikinis, Ron, Berkovsky, Shlomo, Di Ieva, Antonio, Zhang, Liwei, Ji, Nan, and Liu, Sidong

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundThe radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients. ObjectiveThis research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans. MethodsIn this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons. ResultsDNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P

Published
2020
Full Text
View/download PDF

4. Neurosurgery, Explainable AI, and Legal Liability

Author

Matulionyte, Rita, Suero Molina, Eric, Di Ieva, Antonio, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

5. Declaration of Computational Neurosurgery

Author

Di Ieva, Antonio, Suero Molina, Eric, Somerville, Margaret A., Beheshti, Amin, Staartjes, Victor E., Serra, Carlo, Theodore, Nicholas, Elliott, James M., Wesselink, Evert O., Russo, Carlo, Pilitsis, Julie G., Bennett, Christine C., Wu, Shandong, Hammond, Flora M., Lozano, Andres M., Cusimano, Michael D., Davidson, Jennilee M., Castellano, James F., Okonkwo, David O., Arefan, Dooman, Lee, Cheng-Chia, Zanier, Olivier, Da Mutten, Raffaele, Matula, Christian, Rutka, James T., Pease, Matthew, Liu, Sidong, Stummer, Walter, Matulionyte, Rita, Yang, Hongxi, Yuwen, Chang, Cheng, Xuelian, Fan, Hengwei, Wang, Xin, Ge, Zongyuan, Cepeda, Santiago, Sheehan, Jason P., Yang, Joseph Yuan-Mou, Hamer, Ryan P., Cohen-Gadol, Aaron, Hansford, Jordan R., Savage, Greg, Sowman, Paul F., Stewart, Caleb, Kateb, Babak, Sherif, Camillo, Perperidis, Antonios, Guller, Anna, Hanft, Simon, D’Amico, Randy S., Sav, Aydin, Cong, Cong, Song, Yang, Nicolosi, Federico, Wiedmann, Marcus K. H., Barone, Damiano G., Noorani, Imran, Magnussen, John, Krieg, Sandro M., Meling, Torstein R., De Ridder, Dirk, Lawton, Michael T., Rosenfeld, Jeffrey V., Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

6. Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery

Author

Suero Molina, Eric, Black, David, Xie, Andrew, Gill, Jaidev, Di Ieva, Antonio, Stummer, Walter, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

7. Artificial Intelligence in Brain Tumors

Author

Suero Molina, Eric, Azemi, Ghasem, Russo, Carlo, Liu, Sidong, Di Ieva, Antonio, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

8. Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery

Author

Suero Molina, Eric, Di Ieva, Antonio, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

9. Computer Vision in Digital Neuropathology

Author

Cong, Cong, Liu, Sidong, Di Ieva, Antonio, Russo, Carlo, Suero Molina, Eric, Pagnucco, Maurice, Song, Yang, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

10. Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma

Author

Yan, Shenghui, Liu, Sidong, Di Ieva, Antonio, Pagnucco, Maurice, Song, Yang, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

11. Computational Fractal-Based Neurosurgery

Author

Di Ieva, Antonio, Davidson, Jennilee M., Russo, Carlo, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

12. Large Language Models in Neurosurgery

Author

Di Ieva, Antonio, Stewart, Caleb, Suero Molina, Eric, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

13. Artificial Intelligence Methods

Author

Liu, Sidong, Russo, Carlo, Suero Molina, Eric, Di Ieva, Antonio, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

14. Understanding Big Data in Neurosurgery

Author

Beheshti, Amin, Alinejad-Rokny, Hamid, Suero Molina, Eric, Di Ieva, Antonio, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

15. Computational Neurosurgery: Foundation

Author

Di Ieva, Antonio, Suero Molina, Eric, Liu, Sidong, Russo, Carlo, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Rosenhouse-Dantsker, Avia, Editorial Board Member, Di Ieva, Antonio, editor, Suero Molina, Eric, editor, Liu, Sidong, editor, and Russo, Carlo, editor

Published
2024
Full Text
View/download PDF

32. TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification

Author

Yang, Litao, Mehta, Deval, Liu, Sidong, Mahapatra, Dwarikanath, Di Ieva, Antonio, and Ge, Zongyuan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Digital pathology based on whole slide images (WSIs) plays a key role in cancer diagnosis and clinical practice. Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI. In this paper, we aim to learn an optimal patch-level feature space by integrating prototype learning with MIL. To this end, we develop a Trainable Prototype enhanced deep MIL (TPMIL) framework for weakly supervised WSI classification. In contrast to the conventional methods which rely on a certain number of selected patches for feature space refinement, we softly cluster all the instances by allocating them to their corresponding prototypes. Additionally, our method is able to reveal the correlations between different tumor subtypes through distances between corresponding trained prototypes. More importantly, TPMIL also enables to provide a more accurate interpretability based on the distance of the instances from the trained prototypes which serves as an alternative to the conventional attention score-based interpretability. We test our method on two WSI datasets and it achieves a new SOTA. GitHub repository: https://github.com/LitaoYang-Jet/TPMIL, Comment: Accepted for MIDL 2023

Published
2023

33. Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification

Author

Chitnis, Soham Rohit, Liu, Sidong, Dash, Tirtharaj, Verlekar, Tanmay Tulsidas, Di Ieva, Antonio, Berkovsky, Shlomo, Vig, Lovekesh, and Srinivasan, Ashwin

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

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://github.com/soham-chitnis10/WSI-domain-specific., Comment: Accepted in EMBC 2023

Published
2023
Full Text
View/download PDF

35. Cross-Modality Synthesis of T1c MRI from Non-contrast Images Using GANs: Implications for Brain Tumor Research

Author

Tabassum, Mehnaz, Rana, Priyanka, Suero Molina, Eric, Di Ieva, Antonio, Liu, Sidong, 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, Finkelstein, Joseph, editor, Moskovitch, Robert, editor, and Parimbelli, Enea, editor

Published
2024
Full Text
View/download PDF

36. AI in Neuro-Oncology: Predicting EGFR Amplification in Glioblastoma from Whole Slide Images Using Weakly Supervised Deep Learning

Author

Danaei Mehr, Homay, Noorani, Imran, Rana, Priyanka, Di Ieva, Antonio, Liu, Sidong, 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, Finkelstein, Joseph, editor, Moskovitch, Robert, editor, and Parimbelli, Enea, editor

Published
2024
Full Text
View/download PDF

38. The E3 Ubiquitin Ligase SCF Cyclin F Promotes Sequestosome-1/p62 Insolubility and Foci Formation and is Dysregulated in ALS and FTD Pathogenesis

Author

Davidson, Jennilee M., Wu, Sharlynn S. L., Rayner, Stephanie L., Cheng, Flora, Duncan, Kimberley, Russo, Carlo, Newbery, Michelle, Ding, Kunjie, Scherer, Natalie M., Balez, Rachelle, García-Redondo, Alberto, Rábano, Alberto, Rosa-Fernandes, Livia, Ooi, Lezanne, Williams, Kelly L., Morsch, Marco, Blair, Ian P., Di Ieva, Antonio, Yang, Shu, Chung, Roger S., and Lee, Albert

Published
2023
Full Text
View/download PDF

39. A new fractal index to classify forest fragmentation and disorder

Author

Peptenatu, Daniel, Andronache, Ion, Ahammer, Helmut, Radulovic, Marko, Costanza, Jennifer K., Jelinek, Herbert F., Di Ieva, Antonio, Koyama, Kohei, Grecu, Alexandra, Gruia, Andreea Karina, Simion, Adrian-Gabriel, Nedelcu, Iulia Daniela, Olteanu, Cosmin, Drăghici, Cristian-Constantin, Marin, Marian, Diaconu, Daniel Constantin, Fensholt, Rasmus, and Newman, Erica A.

Published
2023
Full Text
View/download PDF

41. Correction to: A new fractal index to classify forest fragmentation and disorder

Author

Peptenatu, Daniel, Andronache, Ion, Ahammer, Helmut, Radulovic, Marko, Costanza, Jennifer K., Jelinek, Herbert F., Di Ieva, Antonio, Koyama, Kohei, Grecu, Alexandra, Gruia, Andreea Karina, Simion, Adrian-Gabriel, Nedelcu, Iulia Daniela, Olteanu, Cosmin, Drăghici, Cristian-Constantin, Marin, Marian, Diaconu, Daniel Constantin, Fensholt, Rasmus, and Newman, Erica A.

Published
2023
Full Text
View/download PDF

42. Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction

Author

Russo, Carlo, Liu, Sidong, and Di Ieva, Antonio

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

Pre-processing and Data Augmentation play an important role in Deep Convolutional Neural Networks (DCNN). Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian space images and volumes. In this work, the spherical coordinates transformation has been applied as a preprocessing method that, used in conjunction with normal MRI volumes, improves the accuracy of brain tumor segmentation and patient overall survival (OS) prediction on Brain Tumor Segmentation (BraTS) Challenge 2020 dataset. The LesionEncoder framework has been then applied to automatically extract features from DCNN models, achieving 0.586 accuracy of OS prediction on the validation data set, which is one of the best results according to BraTS 2020 leaderboard.

Published
2020

43. Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI

Author

Russo, Carlo, Liu, Sidong, and Di Ieva, Antonio

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors. Several automatic or semi-automatic segmentation algorithms have been introduced to segment brain tumors and achieve an expert-like accuracy. Deep Convolutional Neural Networks (DCNN) have recently shown very promising results, however, DCNN models are still far from achieving clinically meaningful results mainly because of the lack of generalization of the models. DCNN models need large annotated datasets to achieve good performance. Models are often optimized on the domain dataset on which they have been trained, and then fail the task when the same model is applied to different datasets from different institutions. One of the reasons is due to the lack of data standardization to adjust for different models and MR machines. In this work, a 3D Spherical coordinates transform during the pre-processing phase has been hypothesized to improve DCNN models' accuracy and to allow more generalizable results even when the model is trained on small and heterogeneous datasets and translated into different domains. Indeed, the spherical coordinate system avoids several standardization issues since it works independently of resolution and imaging settings. Both Cartesian and spherical volumes were evaluated in two DCNN models with the same network structure using the BraTS 2019 dataset. The model trained on spherical transform pre-processed inputs resulted in superior performance over the Cartesian-input trained model on predicting gliomas' segmentation on tumor core and enhancing tumor classes (increase of 0.011 and 0.014 respectively on the validation dataset), achieving a further improvement in accuracy by merging the two models together. Furthermore, the spherical transform is not resolution-dependent and achieve same results on different input resolution., Comment: 26 pages, 5 figures. Submitted to Computer Methods and Programs in Biomedicine

Published
2020

44. Adaptive Unified Contrastive Learning for Imbalanced Classification

Author

Cong, Cong, Yang, Yixing, Liu, Sidong, Pagnucco, Maurice, Di Ieva, Antonio, Berkovsky, Shlomo, Song, Yang, 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, Lian, Chunfeng, editor, Cao, Xiaohuan, editor, Rekik, Islem, editor, Xu, Xuanang, editor, and Cui, Zhiming, editor

Published
2022
Full Text
View/download PDF

49. Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images

Author

Cong, Cong, Liu, Sidong, Di Ieva, Antonio, Pagnucco, Maurice, Berkovsky, Shlomo, Song, Yang, 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, de Bruijne, Marleen, editor, Cattin, Philippe C., editor, Cotin, Stéphane, editor, Padoy, Nicolas, editor, Speidel, Stefanie, editor, Zheng, Yefeng, editor, and Essert, Caroline, editor

Published
2021
Full Text
View/download PDF

50. Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction

Author

Russo, Carlo, Liu, Sidong, Di Ieva, Antonio, 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, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results