Improving Mammography Malignancy Segmentation by Designing the Training Process

Mickael Tardy, Diana Mateus

Abstract: We work on the breast imaging malignancy segmentation task while focusing on the train- ing process instead of network complexity. We designed a training process based on a modified U-Net, increasing the overall segmentation performances by using both, benign and malignant data for training. Our approach makes use of only a small amount of anno- tated data and relies on transfer learning from a self-supervised reconstruction task, and favors explainability.

Paper Type: methodological development

TL;DR: Two-step self- and fully-supervised training process for more precise malignancy segmentation in mammography

Track: short paper

Keywords: Mammography, Segmentation, Malignancy Detection, Explainability

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