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Transfer learning privileged information fuels CAD diagnosis of breast cancer

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Abstract

The efficiency in breast cancer from imaging-based computer-aided diagnosis (CAD) has been revealed in recent years. As a fact, the methods grounded on a single modality constantly lack behind multimodal CAD imaging. However, owing to the restrictions of imaging devices, expressly in rural hospitals, single-modal imaging becomes a favorite in clinical practice for diagnosis. A fresh learning model trending nowadays known as learning using privileged information (LUPI) adopts additional privileged information (PI) modality to help during the training stage, but PI does not contribute in the testing stage. Meanwhile, the link exists between PI and training samples; the same is then reassigned to the learned model. We propose a LUPI-based CAD framework for breast cancer using privileged information in this work. The work offers both a classifier- or feature-level LUPI, in which the information is shifted from the additional PI modality to the diagnosis modality. A thorough comparison has been made among six classifier-level algorithms and six feature-level LUPI algorithms. The experimental results on both the acquired primary datasets show that all classifier-level and deep learning-based feature-level LUPI algorithms can enhance the performance of a single-modal imaging-based CAD for breast cancer by relocating PI.

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  • 27 February 2020

    The articles listed below were published in Issue January 2020, Issue 1, instead of Issue February 2020, Issues 1–2.

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Acknowledgements

Authors are very much thankful to doctors/radiologists and staff team of the Department of Radiology, Jawaharlal Nehru Medical College (JNMC), Aligarh Muslim University, India and medical imaging centers, i.e., Medicare Diagnostic Center and DM diagnostic clinic, Srinagar, India, for verifying all the tissue segmentation results carefully and rating the results on the scale.

Funding

This work is partly supported by the Research Fellowship of the ‘Visvesvaraya Ph.D. Scheme for Electronics & IT,’ Ministry of Electronics and Information technology (MeitY), Government of India (GoI), Vide Grant no. PhD-MLA/4(39)/2015-16.

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  1. Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India

    Tawseef Ayoub Shaikh, Rashid Ali & M. M. Sufyan Beg

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  1. Tawseef Ayoub Shaikh
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Mr. TAS conceived, designed, and performed the experiments. Mr. RA analyzed the data, and he offered his valuable suggestions and feedbacks. Both contributed to reagents/materials/analysis tools and paper writing jointly.

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Shaikh, T.A., Ali, R. & Beg, M.M.S. Transfer learning privileged information fuels CAD diagnosis of breast cancer. Machine Vision and Applications 31, 9 (2020). https://doi.org/10.1007/s00138-020-01058-5

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