33 citations · 80 across the 11 of their papers we have counts for
11 papers
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…
Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective
Zhiqiang Shen, Peng Cao, Junming Su +2
This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-a…
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…
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Qingshan Hou, Peng Cao, Jiaqi Wang +3
Retinal fundus images have been applied for the diagnosis and screening of eye diseases, such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low-qualit…
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…