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20162022
most citedPredicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge

331 citations · 1.9k across the 20 of their papers we have counts for

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16 papers · 1 filter

cs.CV2022

Transformer based multiple instance learning for weakly supervised histopathology image segmentation

Ziniu Qian, Kailu Li, Maode Lai +4

Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates th…

cs.CV2021★ 136 cited

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Shengyu Zhao, Jonathan Cui, Yilun Sheng +4

Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorit…

cs.CV2020

Microscopic fine-grained instance classification through deep attention

Mengran Fan, Tapabrata Chakrabort, Eric I-Chao Chang +2

Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. Deep learning based vision systems mostly d…

cs.CV2020★ 194 cited

MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask

Shengyu Zhao, Yilun Sheng, Yue Dong +2

Feature warping is a core technique in optical flow estimation; however, the ambiguity caused by occluded areas during warping is a major problem that remains unsolved. In this pap…

cs.CV2019★ 305 cited

Recursive Cascaded Networks for Unsupervised Medical Image Registration

Shengyu Zhao, Yue Dong, Eric I-Chao Chang +1

We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration. The proposed architecture is simple in design…

cs.CV2019★ 295 cited

Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network

Shengyu Zhao, Tingfung Lau, Ji Luo +2

3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or…