Pre-biopsy Multi-class Classification of Breast Lesion Pathology in Mammograms

Tal Tlusty, Michal Ozery-Flato, Vesna Barros, Ella Barkan, Mika Amit, David Gruen, Michal Guindy, Tal Arazi, Mona Rozin, Michal Rosen-Zvi, Efrat Hexter

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Characterization of lesions by artificial intelligence (AI) has been the subject of extensive research. In recent years, many studies demonstrated the ability of convolution neural networks (CNNs) to successfully distinguish between malignant and benign breast lesions in mammography (MG) images. However, to date, no study has assessed the specific sub-type of lesions in MG images, as detailed in histolopathology reports. We present a method for finer classification of breast lesions in MG images into multiple pathology sub-types. Our approach works well with radiologists’ diagnostic workflow, and uses data available in radiology reports. The proposed Dual-Radiology Dual-Resolution Network (Du-Rad Du-Res Net) receives dual input from the radiologist and dual image resolutions. The radiologist input includes annotation of the lesion area and semantic radiology features; the dual image resolutions comprise a low resolution of the entire mammogram and a high resolution of the lesion area. The network estimates the likelihood of malignancy, as well as the associated pathological sub-type. We show that the combined input of the lesion region of interest (ROI) and the entire mammogram is important for optimizing the model’s performance. We tested the AI in a reader study on a dataset of 100 heldout cases. The AI outperformed three breast radiologists in the task of lesion histopathology sub-typing.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsChunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages277-286
Number of pages10
ISBN (Print)9783030875886
DOIs
StatePublished - 1 Jan 2021
Externally publishedYes
Event12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12966 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

Keywords

  • Breast cancer
  • Deep neural networks
  • Mammography

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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