PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation
- URL: http://arxiv.org/abs/2410.17812v1
- Date: Wed, 23 Oct 2024 12:17:03 GMT
- Title: PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation
- Authors: Feiyan Feng, Tianyu Liu, Hong Wang, Jun Zhao, Wei Li, Yanshen Sun,
- Abstract summary: Early detection is crucial in mitigating the high mortality rate associated with breast cancer.
locating tumors from low-resolution and high-noise medical images is extremely challenging.
This paper proposes a novel PGDiffSeg that applies diffusion denoising methods to breast cancer medical image segmentation.
- Score: 15.90226610791458
- License:
- Abstract: Early detection through imaging and accurate diagnosis is crucial in mitigating the high mortality rate associated with breast cancer. However, locating tumors from low-resolution and high-noise medical images is extremely challenging. Therefore, this paper proposes a novel PGDiffSeg (Prior-Guided Diffusion Denoising Model with Parameter-Shared Attention) that applies diffusion denoising methods to breast cancer medical image segmentation, accurately recovering the affected areas from Gaussian noise. Firstly, we design a parallel pipeline for noise processing and semantic information processing and propose a parameter-shared attention module (PSA) in multi-layer that seamlessly integrates these two pipelines. This integration empowers PGDiffSeg to incorporate semantic details at multiple levels during the denoising process, producing highly accurate segmentation maps. Secondly, we introduce a guided strategy that leverages prior knowledge to simulate the decision-making process of medical professionals, thereby enhancing the model's ability to locate tumor positions precisely. Finally, we provide the first-ever discussion on the interpretability of the generative diffusion model in the context of breast cancer segmentation. Extensive experiments have demonstrated the superiority of our model over the current state-of-the-art approaches, confirming its effectiveness as a flexible diffusion denoising method suitable for medical image research. Our code will be publicly available later.
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