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Related Experiment Video

Updated: Jun 20, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based

Francesco Manigrasso¹, Rosario Milazzo¹, Alessandro Sebastian Russo¹

¹Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, 10129, Turin, Italy.

Medical Image Analysis

|September 8, 2024

Summary

This summary is machine-generated.

Deep learning models show promise for screening mammography, but challenges remain. Transformer-based architectures perform best, but ensembles of diverse models offer the most robust breast cancer classification.

Keywords:

Computer-aided diagnosis Mammography Visual transformers

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Deep learning (DL) offers potential for automated screening mammography assessment.
Challenges include low cancer prevalence, high-resolution images, and multi-view data integration.
Weakly-supervised learning on exam-level labels is constrained by dataset size and accuracy.

Purpose of the Study:

To evaluate novel transformer-based and graph-based architectures for multi-view mammography.
To compare these against state-of-the-art convolutional neural networks (CNNs).
To assess performance and interpretability in a weakly-supervised setting.

Main Methods:

Extensive evaluation of transformer (ViT) and graph-based architectures.
Comparison with multi-view CNNs on the CSAW dataset.
Weakly-supervised training using exam-level labels on a middle-scale dataset.

Main Results:

Transformer-based architectures demonstrated superior performance.
Different architectures exhibit complementary strengths and weaknesses.
Ensembling diverse architectures yielded more accurate and robust results than single models.

Conclusions:

Multi-view architectures show significant potential for breast cancer classification, even with moderate datasets.
Transformer and graph-based models offer advantages in integrating mammographic views.
Detecting small lesions remains challenging without pixel-level supervision or specialized networks.