5 papers
Can General-Purpose Omnimodels Compete with Specialists? A Case Study in Medical Image Segmentation
Yizhe Zhang, Qiang Chen, Tao Zhou
The emergence of powerful, general-purpose omnimodels capable of processing diverse data modalities has raised a critical question: can these ``jack-of-all-trades'' systems perform…
A Closer Look at Edema Area Segmentation in SD-OCT Images Using Adversarial Framework
Yuhui Tao, Yizhe Zhang, Qiang Chen
The development of artificial intelligence models for macular edema (ME) analy-sis always relies on expert-annotated pixel-level image datasets which are expen-sive to collect pros…
A Dual Radiomic and Dosiomic Filtering Technique for Locoregional Radiation Pneumonitis Prediction in Breast Cancer Patients
Zhenyu Yang, Qian Chen, Rihui Zhang +8
Purpose: Radiation pneumonitis (RP) is a serious complication of intensity-modulated radiation therapy (IMRT) for breast cancer patients, underscoring the need for precise and expl…
Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images
Yutao Hu, Ying Zheng, Shumei Miao +20
Foundation models have demonstrated remarkable potential in medical domain. However, their application to complex cardiovascular diagnostics remains underexplored. In this paper, w…
Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images
Ahjol Senbi, Tianyu Huang, Fei Lyu +8
We explore the feasibility and potential of building a ground-truth-free evaluation model to assess the quality of segmentations generated by the Segment Anything Model (SAM) and i…