Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis

• URL: http://arxiv.org/abs/2309.03506v1
• Date: Thu, 7 Sep 2023 06:33:30 GMT
• Title: Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis
• Authors: Thanh-Huy Nguyen, Quang Hien Kha, Thai Ngoc Toan Truong, Ba Thinh Lam, Ba Hung Ngo, Quang Vinh Dinh, and Nguyen Quoc Khanh Le
• Abstract summary: Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. We propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view. We also propose a simple but novel malignant mammogram synthesis framework for up synthesizing minor class samples.
• Score: 1.1098503592431275
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. In the first problem, many multi-view methods have been released for concatenating features of two or more views for the training and inference stage. Having said that, most multi-view existing methods are not explainable in the meaning of feature fusion, and treat many views equally for diagnosing. Our work aims to propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view (ipsilateral view) before learning the high-level feature that contains the cancerous features. For the second issue, we also propose a simple but novel malignant mammogram synthesis framework for upsampling minor class samples. Our easy-to-implement and no-training framework has eliminated the current limitation of the CutMix algorithm which is unreliable synthesized images with random pasted patches, hard-contour problems, and domain shift problems. Our results on VinDr-Mammo and CMMD datasets show the effectiveness of our two new frameworks for both multi-view training and synthesizing mammographic images, outperforming the previous conventional methods in our experimental settings.

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