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Histo-fusion: a novel domain specific learning to identify invasive ductal carcinoma (IDC) from histopathological images

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Abstract

On the global level, Invasive Ductal Carcinoma (IDC) is one of the leading malignancies among women undergoing breast cancer screening. A slight delay in detecting and diagnosing this disease may result in irreversible complications. Histopathological images obtained from a biopsy examination present enormous structural information, which helps significantly in improving the prognosis of the disease. Pathological analysis involving microscopic examination of the histopathological slides is a highly challenging task due to the limited abilities of conventional computer-aided detection (CAD) methods to reach a precise diagnosis. The problem has become more accentuated due to the less availability of medical data in the public domain. Lately, deep learning methods using artificial neural networks are being used consistently to improve the performance of these CAD methods. Despite less medical data availability, transfer learning has recently been commonly practised to facilitate a deep neural network to train on a specific dataset in resolving the problem. However, the performance of this method is not remarkably appreciable on small and low-resolution medical image datasets compared to those comprising whole slide images of higher resolutions. In this direction, this study proposes a domain-specific learning strategy, Histo-Fusion, with the objective of detecting the IDC more precisely. In the proposed method, the deep CNN model is initially trained on higher-resolution histopathological images of breast tissue which presents ample information for a model to learn the significant features for better discrimination between normal and malignant tissues. Subsequently, through a positive transfer of domain features, the model is further trained on the small and low-resolution images, enabling it to classify these histology images into IDC - and IDC + categories. Moreover, shallow and deep neural network architectures are utilized in the study to compare their performance across the two learning approaches: transfer learning and Histo-Fusion on the IDC dataset. As revealed by the present study results, the proposed Histo-Fusion learning approach has improved each deep CNN’s discriminating abilities by yielding better accuracy scores of around 5% over and above those obtained by the commonly used transfer learning strategy. Therefore, the procedure is expected to reduce false-positive rates and help expert pathologists reach accurate diagnoses.

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Data availability

The data used in the study is freely available and accessible to research community on following links:

1. https://www.kaggle.com/paultimothymooney/breast-histopathology-images

2. https://www.kaggle.com/datasets/ambarish/breakhis/download

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  1. Department of Electronics and Communication Engineering, NIT Srinagar, Srinagar, India

    M. Abdul Jawad & Farida Khursheed

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  1. M. Abdul Jawad
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Jawad, M.A., Khursheed, F. Histo-fusion: a novel domain specific learning to identify invasive ductal carcinoma (IDC) from histopathological images. Multimed Tools Appl 82, 39371–39392 (2023). https://doi.org/10.1007/s11042-023-15134-1

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