858 citations · 1.5k across the 26 of their papers we have counts for
8 papers · 1 filter
Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters
Junde Wu, Huihui Fang, Yehui Yang +5
In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias.…
ExpNet: A unified network for Expert-Level Classification
Junde Wu, Huihui Fang, Yehui Yang +4
Different from the general visual classification, some classification tasks are more challenging as they need the professional categories of the images. In the paper, we call them…
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Junde Wu, Rao Fu, Huihui Fang +5
Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stabl…
Multi-Scale Multi-Target Domain Adaptation for Angle Closure Classification
Zhen Qiu, Yifan Zhang, Fei Li +3
Deep learning (DL) has made significant progress in angle closure classification with anterior segment optical coherence tomography (AS-OCT) images. These AS-OCT images are often a…
Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle
Junde Wu, Huihui Fang, Hoayi Xiong +5
On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the pers…
Towards Lightweight Super-Resolution with Dual Regression Learning
Yong Guo, Mingkui Tan, Zeshuai Deng +5
Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images…
One Hyper-Initializer for All Network Architectures in Medical Image Analysis
Fangxin Shang, Yehui Yang, Dalu Yang +3
Pre-training is essential to deep learning model performance, especially in medical image analysis tasks where limited training data are available. However, existing pre-training m…
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification
Wenshuo Zhou, Dalu Yang, Binghong Wu +6
Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world…