STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation
This addresses the challenge of lesion segmentation for medical diagnosis by incorporating textual knowledge, though it is incremental as it builds on existing vision-language approaches.
The paper tackled the problem of accurate lesion segmentation in medical images by proposing STPNet, a scale-aware text prompt network that leverages vision-language modeling, and it outperformed state-of-the-art methods on datasets like COVID-Xray, COVID-CT, and Kvasir-SEG.
Accurate segmentation of lesions plays a critical role in medical image analysis and diagnosis. Traditional segmentation approaches that rely solely on visual features often struggle with the inherent uncertainty in lesion distribution and size. To address these issues, we propose STPNet, a Scale-aware Text Prompt Network that leverages vision-language modeling to enhance medical image segmentation. Our approach utilizes multi-scale textual descriptions to guide lesion localization and employs retrieval-segmentation joint learning to bridge the semantic gap between visual and linguistic modalities. Crucially, STPNet retrieves relevant textual information from a specialized medical text repository during training, eliminating the need for text input during inference while retaining the benefits of cross-modal learning. We evaluate STPNet on three datasets: COVID-Xray, COVID-CT, and Kvasir-SEG. Experimental results show that our vision-language approach outperforms state-of-the-art segmentation methods, demonstrating the effectiveness of incorporating textual semantic knowledge into medical image analysis. The code has been made publicly on https://github.com/HUANGLIZI/STPNet.