Abstract

Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.

Highlights

  • Breast cancer is currently the second most common cause of cancer-related death in women

  • GB U-Net did not outperform all deep CNN (DCNN) the results demonstrate that transfer learning can be implemented while training DCNNs to segment breast tumor nuclei if precise segmentations are not explicitly required

  • Mask R-convolutional neural networks (CNN) scored slightly lower in Aggregated Jaccard ­Index16 (AJI) and mean average precision (mAP), compared to GB U-Net; one of the main differences between the networks was that Mask R-CNN displayed improved AP at higher intersections over union (IoU) thresholds

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Summary

Introduction

Breast cancer is currently the second most common cause of cancer-related death in women. This study’s objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented. Clinical management and accurate diagnosis of breast cancer type, staging, and grade require tissue biopsies. Researchers and data scientists in AI have applied convolutional neural networks (CNN) and machine classifiers to segment and classify objects, predict disease diagnosis, and treatment response; to tailor medical treatment, develop diagnostic assays, and determine patient response to treatment ­therapies[10]. Training CNNs for semantic or instance segmentation requires exhaustive and full annotations of specific histopathological ­structures[19]

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