7 papers
RefLSM: Linearized Structural-Prior Reflectance Model for Medical Image Segmentation and Bias-Field Correction
Wenqi Zhao, Jiacheng Sang, Fenghua Cheng +3
Medical image segmentation remains challenging due to intensity inhomogeneity, noise, blurred boundaries, and irregular structures. Traditional level set methods, while effective i…
MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis
Yiran Song, Yikai Zhang, Shuang Zhou +6
Multiple instance learning (MIL) has emerged as the dominant paradigm for whole slide image (WSI) analysis in computational pathology, achieving strong diagnostic performance throu…
Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?
Yuxiang Lai, Jike Zhong, Ming Li +2
Recent advances in large generative models have shown that simple autoregressive formulations, when scaled appropriately, can exhibit strong zero-shot generalization across domains…
MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting
Yuheng Li, Yenho Chen, Yuxiang Lai +3
Radiologic diagnostic errors-under-reading errors, inattentional blindness, and communication failures-remain prevalent in clinical practice. These issues often stem from missed lo…
Detail++: Training-Free Detail Enhancer for T2I Diffusion Models
Lifeng Chen, Jiner Wang, Zihao Pan +3
Recent advances in text-to-image (T2I) generation have led to impressive visual results. However, these models still face significant challenges when handling complex prompt, parti…
RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT
Yuheng Li, Mingzhe Hu, Richard L. J. Qiu +4
Deep learning-based segmentation of genito-pelvic structures in MRI and CT is crucial for applications such as radiation therapy, surgical planning, and disease diagnosis. However,…
Towards Universal Text-driven CT Image Segmentation
Yuheng Li, Yuxiang Lai, Maria Thor +4
Computed tomography (CT) is extensively used for accurate visualization and segmentation of organs and lesions. While deep learning models such as convolutional neural networks (CN…