Understanding Stain Separation Improves Cross-Scanner Adenocarcinoma Segmentation with Joint Multi-Task Learning

• URL: http://arxiv.org/abs/2409.13246v1
• Date: Fri, 20 Sep 2024 06:12:52 GMT
• Title: Understanding Stain Separation Improves Cross-Scanner Adenocarcinoma Segmentation with Joint Multi-Task Learning
• Authors: Ho Heon Kim, Won Chan Jeong, Young Shin Ko, Young Jin Park,
• Abstract summary: Digital pathology has made significant advances in tumor diagnosis and segmentation, but image variability limits the effectiveness of current algorithms. The COSAS (Cross-Organ and Cross-Scanner Adenocarcinoma) challenge addresses this issue by improving the resilience of segmentation algorithms to domain shift. Our approach employs unsupervised learning through stain separation within a multi-task learning framework using a multi-decoder autoencoder.
• Score: 2.3470559413988936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital pathology has made significant advances in tumor diagnosis and segmentation, but image variability due to differences in organs, tissue preparation, and acquisition - known as domain shift - limits the effectiveness of current algorithms. The COSAS (Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation) challenge addresses this issue by improving the resilience of segmentation algorithms to domain shift, with Task 2 focusing on adenocarcinoma segmentation using a diverse dataset from six scanners, pushing the boundaries of clinical diagnostics. Our approach employs unsupervised learning through stain separation within a multi-task learning framework using a multi-decoder autoencoder. This model isolates stain matrix and stain density, allowing it to handle color variation and improve generalization across scanners. We further enhanced the robustness of the model with a mixture of stain augmentation techniques and used a U-net architecture for segmentation. The novelty of our method lies in the use of stain separation within a multi-task learning framework, which effectively disentangles histological structures from color variations. This approach shows promise for improving segmentation accuracy and generalization across different histopathological stains, paving the way for more reliable diagnostic tools in digital pathology.

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