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StethoNet: Robust Breast Cancer Mammography Classification Framework
  • +3
  • Charalampos Lamprou,
  • Kyriaki Katsikari,
  • Noora A Rahmani,
  • Leontios J Hadjileontiadis,
  • Mohamed L Seghier,
  • Aamna Alshehhi
Charalampos Lamprou

Corresponding Author:[email protected]

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Kyriaki Katsikari
Noora A Rahmani
Leontios J Hadjileontiadis
Mohamed L Seghier
Aamna Alshehhi

Abstract

Despite the emergence of numerous Deep Learning (DL) models for breast cancer detection via mammograms, there is a lack of evidence about their robustness to perform well on new unseen mammograms. To fill this gap, we introduce StethoNet, a DL-based framework that consists of multiple Convolutional Neural Network (CNN) trained models for classifying benign and malignant tumors. StethoNet was trained on the Chinese Mammography Database (CMMD), and tested on unseen images from CMMD, as well as on images from two independent datasets, i.e., the Vindr-Mammo and the INbreast datasets. To mitigate domain-shift effects, we applied an effective entropy-based domain adaptation technique at the preprocessing stage. Furthermore, a Bayesian hyperparameters optimization scheme was implemented for StethoNet optimization. To ensure interpretable results that corroborate with prior clinical knowledge, attention maps generated using Gradientweighted Class Activation Mapping (GRAD-CAM) were compared with Regions of Interest (ROIs) identified by radiologists. StethoNet achieved impressive Area Under the receiver operating characteristics Curve (AUC) scores: 90.7% (88.6%-92.8%), 83.9% (76.0%-91.8%), and 85.7% (82.1%-89.4%) for the CMMD, INbreast, and Vindr-Mammo datasets, respectively. These results surpass the current state of the art and highlight the robustness and generalizability of StethoNet, scaffolding the integration of DL models into breast cancer mammography screening workflows. Our code is available at https://github.com/CharLamp10/breast cancer detection.git.
06 Feb 2024Submitted to TechRxiv
12 Feb 2024Published in TechRxiv