46 citations · 119 across the 32 of their papers we have counts for
11 papers · 2 filters
LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models
Haolin Li, Yuhang Zhou, Ziheng Zhao +5
The widespread adoption of large-scale pre-training techniques has significantly advanced the development of medical foundation models, enabling them to serve as versatile tools ac…
Reprogramming Distillation for Medical Foundation Models
Yuhang Zhou, Siyuan Du, Haolin Li +3
Medical foundation models pre-trained on large-scale datasets have demonstrated powerful versatile capabilities for various tasks. However, due to the gap between pre-training task…
Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters
Yuhang Zhou, Zihua Zhao, Haolin Li +4
Training a unified model to take multiple targets into account is a trend towards artificial general intelligence. However, how to efficiently mitigate the training conflicts among…
Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning
Zihua Zhao, Mengxi Chen, Tianjie Dai +4
Noisy correspondence that refers to mismatches in cross-modal data pairs, is prevalent on human-annotated or web-crawled datasets. Prior approaches to leverage such data mainly con…
Low-Rank Knowledge Decomposition for Medical Foundation Models
Yuhang Zhou, Haolin Li, Siyuan Du +3
The popularity of large-scale pre-training has promoted the development of medical foundation models. However, some studies have shown that although foundation models exhibit stron…
RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis
Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao +4
Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing…
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning
Aofan Jiang, Chaoqin Huang, Qing Cao +5
The electrocardiogram (ECG) is an essential tool for diagnosing heart disease, with computer-aided systems improving diagnostic accuracy and reducing healthcare costs. Despite adva…
Knowledge-enhanced Visual-Language Pretraining for Computational Pathology
Xiao Zhou, Xiaoman Zhang, Chaoyi Wu +3
In this paper, we consider the problem of visual representation learning for computational pathology, by exploiting large-scale image-text pairs gathered from public resources, alo…
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images
Chaoqin Huang, Aofan Jiang, Jinghao Feng +3
Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However, th…
ReMamber: Referring Image Segmentation with Mamba Twister
Yuhuan Yang, Chaofan Ma, Jiangchao Yao +3
Referring Image Segmentation~(RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks. However, the quadratic computation cos…
Learning-based Bone Quality Classification Method for Spinal Metastasis
Shiqi Peng, Bolin Lai, Guangyu Yao +4
Spinal metastasis is the most common disease in bone metastasis and may cause pain, instability and neurological injuries. Early detection of spinal metastasis is critical for accu…