60 citations · 77 across the 9 of their papers we have counts for
9 papers
Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection
Yi Gu, Yi Lin, Kwang-Ting Cheng +1
Medical anomaly detection (AD) is crucial in pathological identification and localization. Current methods typically rely on uncertainty estimation in deep ensembles to detect anom…
Aligning Medical Images with General Knowledge from Large Language Models
Xiao Fang, Yi Lin, Dong Zhang +2
Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising gene…
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
Tianwei Zhang, Dong Wei, Mengmeng Zhu +2
Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The rel…
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification
Shuhan Li, Yi Lin, Hao Chen +1
Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and…
Prompt-Guided Foundation Model Tuning for Pathology Image Classification
Yi Lin, Zhengjie Zhu, Kwang-Ting Cheng +1
Foundation models have become pivotal in advancing computational pathology, particularly for whole slide image (WSI) classification. However, prevailing methodologies often rely on…
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective
Yu Cai, Hao Chen, Kwang-Ting Cheng
Medical anomaly detection aims to identify abnormal findings using only normal training data, playing a crucial role in health screening and recognizing rare diseases. Reconstructi…
BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
Yi Lin, Zeyu Wang, Dong Zhang +2
Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of t…
More complex encoder is not all you need
Weibin Yang, Longwei Xu, Pengwei Wang +4
U-Net and its variants have been widely used in medical image segmentation. However, most current U-Net variants confine their improvement strategies to building more complex encod…
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Yu Cai, Hao Chen, Xin Yang +2
Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most m…