Mammography Assessment using Multi-Scale Deep Classifiers

TL;DR

This paper introduces a multi-scale deep classification approach for mammography assessment that improves tissue localization and causal pixel identification, achieving high accuracy and clinical relevance.

Contribution

It proposes a novel tissue classification framework that enhances localization and causal pixel detection in mammography using high-resolution heatmaps and deep learning.

Findings

AUC above 0.9 for tissue classification

Effective localization of causal regions

Augmentation of heatmap regression with local classifiers

Abstract

Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in pushing beyond approximately bounding regions. We propose a classification approach grounded in high performance tissue assessment as an alternative to all-in-one localization and assessment models that is also capable of pinpointing the causal pixels. First, the objective of the mammography assessment task is formalized in the context of local tissue classifiers. Then, the accuracy of a convolutional neural net is evaluated on classifying patches of tissue with suspicious findings at varying scales, where highest obtained AUC is above $0.9$. The local evaluations of one such expert tissue classifier is used to augment the results of a heatmap regression model and additionally recover the exact causal regions at high resolution as a saliency image suitable for clinical settings.