Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models

Autor:	Liu, Houze, Zhou, Tong, Xiang, Yanlin, Shen, Aoran, Hu, Jiacheng, Du, Junliang
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Medical image segmentation is crucial for accurate clinical diagnoses, yet it faces challenges such as low contrast between lesions and normal tissues, unclear boundaries, and high variability across patients. Deep learning has improved segmentation accuracy and efficiency, but it still relies heavily on expert annotations and struggles with the complexities of medical images. The small size of medical image datasets and the high cost of data acquisition further limit the performance of segmentation networks. Diffusion models, with their iterative denoising process, offer a promising alternative for better detail capture in segmentation. However, they face difficulties in accurately segmenting small targets and maintaining the precision of boundary details. This article discusses the importance of medical image segmentation, the limitations of current deep learning approaches, and the potential of diffusion models to address these challenges. Comment: After a peer review process for a journal submission, we have been told the main conclusions presented in this paper have been proven previously by others. I believe the paper should be withdrawn
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.14353 Zobrazit plný text záznamu View this record from Arxiv