6 papers
An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification
Chengkun Sun, Jinqian Pan, Renjie Liang +7
Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and trea…
CIPHER: Causal Intervention Pathways for Healthcare Equity and Robustness
Xinyu Jia, Weidong Guo, Wangyuan Zhao +3
Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.g., race, sex), even when average accuracy is high.…
Enhancing Brain MRI Anomaly Detection and Reasoning with ROI Rethink and Synthetic Data
Shangkun Li, Jie Xu, Yi Guo +2
Medical vision-language models typically generate diagnoses through single-pass inference without indicating which image regions support their conclusions. This lack of spatial gro…
EchoAgent: Towards Reliable Echocardiography Interpretation with "Eyes","Hands" and "Minds"
Qin Wang, Zhiqing He, Yu Liu +8
Reliable interpretation of echocardiography (Echo) is crucial for assessing cardiac function, which demands clinicians to synchronously orchestrate multiple capabilities, including…
SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting
Jie Xu, Xiaokang Li, Chengyu Yue +2
Medical image segmentation often faces the challenge of prohibitively expensive annotation costs. While few-shot learning offers a promising solution to alleviate this burden, conv…
Diff-CXR: Report-to-CXR generation through a disease-knowledge enhanced diffusion model
Peng Huang, Bowen Guo, Shuyu Liang +3
Text-To-Image (TTI) generation is significant for controlled and diverse image generation with broad potential applications. Although current medical TTI methods have made some pro…