4 citations · 6 across the 3 of their papers we have counts for
4 papers
Skill-Evolving Grounded Reasoning for Free-Text Promptable 3D Medical Image Segmentation
Tongrui Zhang, Chenhui Wang, Yongming Li +3
Free-text promptable 3D medical image segmentation offers an intuitive and clinically flexible interaction paradigm. However, current methods are highly sensitive to linguistic var…
A Fully Transformer Based Multimodal Framework for Explainable Cancer Image Segmentation Using Radiology Reports
Enobong Adahada, Isabel Sassoon, Kate Hone +1
We introduce Med-CTX, a fully transformer based multimodal framework for explainable breast cancer ultrasound segmentation. We integrate clinical radiology reports to boost both pe…
Segmenting Medical Images: From UNet to Res-UNet and nnUNet
Lina Huang, Alina Miron, Kate Hone +1
This study provides a comparative analysis of deep learning models including UNet, Res-UNet, Attention Res-UNet, and nnUNet, and evaluates their performance in brain tumour, polyp,…
Performance Analysis of UNet and Variants for Medical Image Segmentation
Walid Ehab, Yongmin Li
Medical imaging plays a crucial role in modern healthcare by providing non-invasive visualisation of internal structures and abnormalities, enabling early disease detection, accura…