A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification for Breast Cancer Detection in Mammograms

Abstract

Mammography, an X-ray–based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intelligence have enabled increasingly sophisticated computer-aided diagnostic methods, evolving from patch-based classifiers to whole-image approaches and then to multi-view architectures that jointly analyze complementary projections. Despite this progress, several critical questions remain unanswered. Although individual aspects of CNN-based mammogram classification—such as patch pretraining, backbone transferability, and multi-view fusion—have been studied separately in prior work, no unified comparison has examined them under controlled conditions across datasets of contrasting image quality. In this study, we close that gap through a systematic empirical investigation organized around five interrelated questions: (1) the role of patch classifiers, (2) the transferability of natural-image-trained backbones, (3) the advantages of “learn-to-resize” over conventional downscaling, (4) the contribution of multi-view integration, and (5) the robustness of findings across varying image quality. Questions (3) and (5) are addressed here for the first time, while questions (1), (2), and (4) are re-examined within a unified protocol that reveals dataset-quality dependencies invisible to prior single-dataset studies. Beyond benchmarking, our experiments demonstrate clear performance gains over prior work. For the CBIS-DDSM dataset, we improved single-view AUC from 0.8153 to 0.8343, and multiple-view AUC from 0.8483 to 0.8658. We also introduce a comparative protocol that places single- and multi-view classifiers on the same evaluation footing—by aggregating per-view single-view predictions into per-exam scores—enabling a statistically valid comparison between the two; using this protocol, we measured a 0.0217 AUC gain from extending single- to multi-view analysis on CBIS-DDSM. On the complete VinDr-Mammo dataset, the multiple-view approach further improved results, achieving a 0.0492 AUC increase over single view and reaching 0.8511 AUC overall. These results establish new state-of-the-art benchmarks, providing clear evidence of the advantages of multi-view architectures for mammogram interpretation. Beyond performance, our analysis offers principled insights into model design and transfer learning strategies, contributing to the development of more accurate and reliable breast cancer screening tools. The inference code and trained models are publicly available at https://github.com/dpetrini/multiple-view.