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2,435 results on '"Dou, Qi"'

207. Multicontrast MRI Super-Resolution via Transformer-Empowered Multiscale Contextual Matching and Aggregation

Author

Lyu, Jun, Li, Guangyuan, Wang, Chengyan, Cai, Qing, Dou, Qi, Zhang, David, and Qin, Jing

Abstract

Magnetic resonance imaging (MRI) possesses the unique versatility to acquire images under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution (SR) techniques possible and needful. Compared with single-contrast MRI SR, multicontrast SR is expected to produce higher quality images by exploiting a variety of complementary information embedded in different imaging contrasts. However, existing approaches still have two shortcomings: 1) most of them are convolution-based methods and, hence, weak in capturing long-range dependencies, which are essential for MR images with complicated anatomical patterns and 2) they ignore to make full use of the multicontrast features at different scales and lack effective modules to match and aggregate these features for faithful SR. To address these issues, we develop a novel multicontrast MRI SR network via transformer-empowered multiscale feature matching and aggregation, dubbed McMRSR $^{++}$ . First, we tame transformers to model long-range dependencies in both reference and target images at different scales. Then, a novel multiscale feature matching and aggregation method is proposed to transfer corresponding contexts from reference features at different scales to the target features and interactively aggregate them. Furthermore, a texture-preserving branch and a contrastive constraint are incorporated into our framework for enhancing the textural details in the SR images. Experimental results on both public and clinical in vivo datasets show that McMRSR $^{++}$ outperforms state-of-the-art methods under peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), and root mean square error (RMSE) metrics significantly. Visual results demonstrate the superiority of our method in restoring structures, demonstrating its great potential to improve scan efficiency in clinical practice.

Published
2024
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208. Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane

Author

Yang, Chen, Wang, Kailing, Wang, Yuehao, Dou, Qi, Yang, Xiaokang, and Shen, Wei

Abstract

Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems. Existing methods either compromise on rendering quality or are excessively computationally intensive, often demanding dozens of hours to perform, which significantly hinders their practical application. In this paper, we introduce Fast Orthogonal Plane (Forplane), a novel, efficient framework based on neural radiance fields (NeRF) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of orthogonal neural planes. This factorization discretizes the four-dimensional space, leading to a decreased memory usage and faster optimization. A spatiotemporal importance sampling scheme is introduced to improve performance in regions with tool occlusion as well as large motions and accelerate training. An efficient ray marching method is applied to skip sampling among empty regions, significantly improving inference speed. Forplane accommodates both binocular and monocular endoscopy videos, demonstrating its extensive applicability and flexibility. Our experiments, carried out on two in vivo datasets, the EndoNeRF and Hamlyn datasets, demonstrate the effectiveness of our framework. In all cases, Forplane substantially accelerates both the optimization process (by over 100 times) and the inference process (by over 15 times) while maintaining or even improving the quality across a variety of non-rigid deformations. This significant performance improvement promises to be a valuable asset for future intraoperative surgical applications. The code of our project is now available at https://github.com/Loping151/ForPlane.

Published
2024
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209. Breast Cancer Classification from Digital Pathology Images via Connectivity-aware Graph Transformer

Author

Wang, Kang, Zheng, Feiyang, Cheng, Lan, Dai, Hong-Ning, Dou, Qi, Qin, Jing, Wang, Kang, Zheng, Feiyang, Cheng, Lan, Dai, Hong-Ning, Dou, Qi, and Qin, Jing

Abstract

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT. IEEE

Published
2024

210. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Author

Sudre, Carole H; https://orcid.org/0000-0001-5753-428X, Van Wijnen, Kimberlin, Dubost, Florian, Adams, Hieab, Atkinson, David, Barkhof, Frederik, Birhanu, Mahlet A, Bron, Esther E, Camarasa, Robin, Chaturvedi, Nish, Chen, Yuan, Chen, Zihao, Chen, Shuai, Dou, Qi, Evans, Tavia, Ezhov, Ivan, Gao, Haojun, Girones Sanguesa, Marta, Gispert, Juan Domingo, Gomez Anson, Beatriz, Hughes, Alun D, Ikram, M Arfan, Ingala, Silvia, Jaeger, H Rolf, Kofler, Florian, Kuijf, Hugo J, Kutnar, Denis, Lee, Minho, Li, Bo, Lorenzini, Luigi, et al, Sudre, Carole H; https://orcid.org/0000-0001-5753-428X, Van Wijnen, Kimberlin, Dubost, Florian, Adams, Hieab, Atkinson, David, Barkhof, Frederik, Birhanu, Mahlet A, Bron, Esther E, Camarasa, Robin, Chaturvedi, Nish, Chen, Yuan, Chen, Zihao, Chen, Shuai, Dou, Qi, Evans, Tavia, Ezhov, Ivan, Gao, Haojun, Girones Sanguesa, Marta, Gispert, Juan Domingo, Gomez Anson, Beatriz, Hughes, Alun D, Ikram, M Arfan, Ingala, Silvia, Jaeger, H Rolf, Kofler, Florian, Kuijf, Hugo J, Kutnar, Denis, Lee, Minho, Li, Bo, Lorenzini, Luigi, and et al

Abstract

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Published
2024

211. Efficient Data-driven Scene Simulation using Robotic Surgery Videos via Physics-embedded 3D Gaussians

Author

Yang, Zhenya, Chen, Kai, Long, Yonghao, Dou, Qi, Yang, Zhenya, Chen, Kai, Long, Yonghao, and Dou, Qi

Abstract

Surgical scene simulation plays a crucial role in surgical education and simulator-based robot learning. Traditional approaches for creating these environments with surgical scene involve a labor-intensive process where designers hand-craft tissues models with textures and geometries for soft body simulations. This manual approach is not only time-consuming but also limited in the scalability and realism. In contrast, data-driven simulation offers a compelling alternative. It has the potential to automatically reconstruct 3D surgical scenes from real-world surgical video data, followed by the application of soft body physics. This area, however, is relatively uncharted. In our research, we introduce 3D Gaussian as a learnable representation for surgical scene, which is learned from stereo endoscopic video. To prevent over-fitting and ensure the geometrical correctness of these scenes, we incorporate depth supervision and anisotropy regularization into the Gaussian learning process. Furthermore, we apply the Material Point Method, which is integrated with physical properties, to the 3D Gaussians to achieve realistic scene deformations. Our method was evaluated on our collected in-house and public surgical videos datasets. Results show that it can reconstruct and simulate surgical scenes from endoscopic videos efficiently-taking only a few minutes to reconstruct the surgical scene-and produce both visually and physically plausible deformations at a speed approaching real-time. The results demonstrate great potential of our proposed method to enhance the efficiency and variety of simulations available for surgical education and robot learning.

Published
2024

212. Unsupervised Domain Adaptation of ConvNets for Medical Image Segmentation via Adversarial Learning

Author

Dou, Qi, Chen, Cheng, Ouyang, Cheng, Chen, Hao, Heng, Pheng Ann, Kang, Sing Bing, Series Editor, Singh, Sameer, Founding Editor, Bischof, Horst, Advisory Editor, Bowden, Richard, Advisory Editor, Dickinson, Sven, Advisory Editor, Jia, Jiaya, Advisory Editor, Lee, Kyoung Mu, Advisory Editor, Sato, Yoichi, Advisory Editor, Schiele, Bernt, Advisory Editor, Sclaroff, Stan, Advisory Editor, Lu, Le, editor, Wang, Xiaosong, editor, Carneiro, Gustavo, editor, and Yang, Lin, editor

Published
2019
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213. A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network

Author

Liu, Zhiyu, Jiang, Wenhao, Lee, Kit-Hang, Lo, Yat-Long, Ng, Yui-Lun, Dou, Qi, Vardhanabhuti, Varut, Kwok, Ka-Wai, 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, Nguyen, Dan, editor, Xing, Lei, editor, and Jiang, Steve, editor

Published
2019
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214. Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

Author

Chen, Cheng, Dou, Qi, Jin, Yueming, Chen, Hao, Qin, Jing, Heng, Pheng-Ann, 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, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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217. Prothrombin Complex Concentrate vs Conservative Management in ICH Associated With Direct Oral Anticoagulants

Author

Ip, Bonaventure, primary, Pan, Sangqi, additional, Yuan, Zhong, additional, Hung, Trista, additional, Ko, Ho, additional, Leng, Xinyi, additional, Liu, Yuying, additional, Li, Shuang, additional, Lee, Sing Yau, additional, Cheng, Cyrus, additional, Chan, Howard, additional, Mok, Vincent, additional, Soo, Yannie, additional, Wu, Xiaoli, additional, Lui, Leong Ting, additional, Chan, Rosa, additional, Abrigo, Jill, additional, Dou, Qi, additional, Seiffge, David, additional, and Leung, Thomas, additional

Published
2024
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224. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Author

Dou, Qi, So, Tiffany Y., Jiang, Meirui, Liu, Quande, Vardhanabhuti, Varut, Kaissis, Georgios, Li, Zeju, Si, Weixin, Lee, Heather H. C., Yu, Kevin, Feng, Zuxin, Dong, Li, Burian, Egon, Jungmann, Friederike, Braren, Rickmer, Makowski, Marcus, Kainz, Bernhard, Rueckert, Daniel, Glocker, Ben, Yu, Simon C. H., and Heng, Pheng Ann

Published
2021
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225. MTMR-Net: Multi-task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis

Author

Liu, Lihao, Dou, Qi, Chen, Hao, Olatunji, Iyiola E., Qin, Jing, Heng, Pheng-Ann, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Stoyanov, Danail, editor, Taylor, Zeike, editor, Carneiro, Gustavo, editor, Syeda-Mahmood, Tanveer, editor, Martel, Anne, editor, Maier-Hein, Lena, editor, Tavares, João Manuel R.S., editor, Bradley, Andrew, editor, Papa, João Paulo, editor, Belagiannis, Vasileios, editor, Nascimento, Jacinto C., editor, Lu, Zhi, editor, Conjeti, Sailesh, editor, Moradi, Mehdi, editor, Greenspan, Hayit, editor, and Madabhushi, Anant, editor

Published
2018
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235. Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage

Author

Ma, Wenao, Chen, Cheng, Gong, Yuqi, Chan, Nga Yan, Jiang, Meirui, Mak, Calvin Hoi-Kwan, Abrigo, Jill M., and Dou, Qi

Abstract

Aneurysmal subarachnoid hemorrhage is a medical emergency of brain that has high mortality and poor prognosis. Causal effect estimation of treatment strategies on patient outcomes is crucial for aneurysmal subarachnoid hemorrhage treatment decision-making. However, most existing studies on treatment decision-making support of this disease are unable to simultaneously compare the potential outcomes of different treatments for a patient. Furthermore, these studies fail to harmoniously integrate the imaging data with non-imaging clinical data, both of which are useful in clinical scenarios. In this paper, we estimate the causal effect of various treatments on patients with aneurysmal subarachnoid hemorrhage by integrating plain CT with non-imaging clinical data, which is represented using structured tabular data. Specifically, we first propose a novel scheme that uses multi-modality confounders distillation architecture to predict the treatment outcome and treatment assignment simultaneously. With these distilled confounder features, we design an imaging and non-imaging interaction representation learning strategy to use the complementary information extracted from different modalities to balance the feature distribution of different treatment groups. We have conducted extensive experiments using a clinical dataset of 656 subarachnoid hemorrhage cases, which was collected from the Hospital Authority Data Collaboration Laboratory in Hong Kong. Our method shows consistent improvements on the evaluation metrics of treatment effect estimation, achieving state-of-the-art results over strong competitors. Code is released at https://github.com/med-air/TOP-aSAH.

Published
2024
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242. Contributors

Author

Akbilgic, Oguz, primary, AlAnzi, Turki, additional, An, Xiangdong, additional, Barrio, Jorge R., additional, Bertoldo, Alessandra, additional, Bi, Lei, additional, Bin, Sheng, additional, Cai, Weidong, additional, Cao, Longbing, additional, Chen, Hao, additional, Clarke, Robert, additional, Cobelli, Claudio, additional, Cui, Hui, additional, Dou, Qi, additional, Du, Yiping P., additional, Eberl, Stefan, additional, Fan, Ming, additional, Feng, Shi-Hao, additional, Feng, David Dagan, additional, Fischer, Gregory S., additional, Fu, Yi, additional, Fulham, Michael J., additional, Gao, Fan, additional, Hao, Manzhao, additional, Heng, Pheng-Ann, additional, Huang, Sung-Cheng, additional, Huang, H.K., additional, Huang, Jimmy Xiangji, additional, Istepanian, Robert S.H., additional, Jung, Younhyun, additional, Kamaleswaran, Rishikesan, additional, Kazanzides, Peter, additional, Kim, Jinman, additional, Kim, Minjeong, additional, Kong, Bin, additional, Kumar, Ashnil, additional, Lan, Ning, additional, Li, Zhongyu, additional, Li, Lihua, additional, Liu, Brent J., additional, Ma, Junbo, additional, Mao, Chunhong, additional, Masood, Saleha, additional, Mun, Seong K., additional, Pan, Yuxiang, additional, Qin, Jing, additional, Ressom, Habtom W., additional, Schoenewald, Caroline, additional, Shaban-Nejad, Arash, additional, Shen, Hong-Bin, additional, Shin, Eun Kyong, additional, Simaan, Nabil, additional, Smith, Nadine, additional, Song, Yang, additional, Taylor, Russell H., additional, Tsai, Tsung-Heng, additional, Tu, Jiawei, additional, Urbin, Michael A., additional, Wan, Hao, additional, Wang, Hao, additional, Wang, Yuqi, additional, Wang, Qian, additional, Wang, Xiuying, additional, Wang, Minkun, additional, Wang, Ping, additional, Wang, Yue, additional, Webb, Andrew, additional, Weber, Douglas J., additional, Wen, Lingfeng, additional, Wu, Anna M., additional, Wu, Guorong, additional, Xu, Jia-Yan, additional, Yan, Chenggang, additional, Yan, Ke, additional, Yang, Defu, additional, Zhang, Xiaofeng, additional, Zhang, Shaoting, additional, Zhang, Bin, additional, Zhang, Zhen, additional, Zhu, Yitan, additional, Zhuang, Liujing, additional, and Zuo, Wangmeng, additional

Published
2020
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244. Contributors

Author

Abolmaesumi, Purang, primary, Ahmad, Sahar, additional, Anderson, William S., additional, Asman, Andrew J., additional, Ben-Cohen, Avi, additional, Biswas, Pradipta, additional, Boctor, Emad, additional, Bodenstedt, Sebastian, additional, Breton, Elodie, additional, Cao, Xiaohuan, additional, Carass, Aaron, additional, Cardoso, M. Jorge, additional, Castillo Tovar, Jose M., additional, Chen, Elvis C.S., additional, Chen, Hao, additional, Chen, Zhi, additional, Cheng, Jie-Zhi, additional, Cootes, T.F., additional, Davatzikos, Christos, additional, de Ribaupierre, Sandrine, additional, Dewey, Blake E., additional, DiPietro, Robert, additional, Dong, Pei, additional, Dou, Qi, additional, Eagleson, Roy, additional, Elson, Daniel S., additional, Erus, Guray, additional, Esposito, M., additional, Essert, Caroline, additional, Fan, Jingfan, additional, Fenster, Aaron, additional, Fichtinger, Gabor, additional, Gangi, Afshin, additional, Garnon, Julien, additional, Gilbert, Kathleen, additional, Glocker, Ben, additional, Greenspan, Hayit, additional, Habes, Mohamad, additional, Hacihaliloglu, Ilker, additional, Hager, Gregory D., additional, Hammernik, Kerstin, additional, Heinrich, Mattias P., additional, Heng, Pheng-Ann, additional, Hennersperger, C., additional, Hensen, Bennet, additional, Holden, Matthew, additional, Huo, Yuankai, additional, Ilse, Maximilian, additional, Išgum, Ivana, additional, Joskowicz, Leo, additional, Judy, Brendan F., additional, Kägebein, Urte, additional, Kamnitsas, Konstantinos, additional, Kashyap, Satyananda, additional, Kazanzides, Peter, additional, Klein, Stefan, additional, Knoll, Florian, additional, Konukoglu, Ender, additional, Kulkarni, Pankaj Pramod, additional, Landman, Bennett A., additional, Lasso, Andras, additional, Ledig, Christian, additional, Lee, Kyungmoo, additional, Linte, Cristian A., additional, Lu, Le, additional, Lyu, Ilwoo, additional, Maier-Hein, Lena, additional, Metaxas, Dimitris N., additional, Miller, Karol, additional, Modat, Marc, additional, Mori, Kensaku, additional, Moriakov, Nikita, additional, Mousavi, Parvin, additional, Navab, N., additional, Niessen, Wiro J., additional, Ortiz, Marilu, additional, Ourselin, Sebastien, additional, Papież, Bartłomiej W., additional, Peng, Yifan, additional, Pham, Dzung L., additional, Pontre, Beau, additional, Prince, Jerry L., additional, Qi, Xiaojuan, additional, Rothgang, Eva, additional, Roy, Snehashis, additional, Schafer, Sebastian, additional, Shen, Dinggang, additional, Sikander, Sakura, additional, Siewerdsen, Jeffrey H., additional, Song, Sang-Eun, additional, Sonka, Milan, additional, Speidel, Stefanie, additional, Starmans, Martijn P.A., additional, Stefan, P., additional, Stoyanov, Danail, additional, Sudre, Carole H., additional, Suinesiaputra, Avan, additional, Taylor, Russell H., additional, Teuwen, Jonas, additional, Tomczak, Jakub M., additional, Traub, J., additional, Ungi, Tamas, additional, van der Voort, Sebastian R., additional, Vasconcelos, Francisco, additional, Vedula, S. Swaroop, additional, Veenland, Jifke F., additional, Wacker, Frank K., additional, Wang, Xiaosong, additional, Welling, Max, additional, Wittek, Adam, additional, Wolterink, Jelmer M., additional, Xiong, Tao, additional, Xu, Daguang, additional, Xu, Zhoubing, additional, Yan, Zhennan, additional, Yang, Dong, additional, Yang, Lin, additional, Yap, Pew-Thian, additional, Young, Alistair A., additional, Zevin, Boris, additional, Zhang, Honghai, additional, Zhang, Zizhao, additional, Zhao, Can, additional, Zheng, Yefeng, additional, and Zhou, S. Kevin, additional

Published
2020
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