2.4k citations · 2.7k across the 35 of their papers we have counts for
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Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation
Junming Su, Zhiqiang Shen, Peng Cao +2
The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate…
Adaptive Mix for Semi-Supervised Medical Image Segmentation
Zhiqiang Shen, Peng Cao, Junming Su +2
Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, blending two or more images to generate strong-perturbed samples for strong-weak ps…
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Qingshan Hou, Shuai Cheng, Peng Cao +4
Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and…
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Zhiqiang Shen, Peng Cao, Hua Yang +3
Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models te…
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Haonan Wang, Peng Cao, Jiaqi Wang +1
Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to mo…
U-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Xuebin Qin, Zichen Zhang, Chenyang Huang +3
In this paper, we design a simple yet powerful deep network architecture, U-Net, for salient object detection (SOD). The architecture of our U-Net is a two-level nested U-s…