Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks

Qicheng Lao and Thomas Fevens

Abstract

In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined. However, previous research on such classification tasks using convolutional neural networks primarily determine a diagnosis for a single magnification level. In this paper, we propose a case-based approach using deep residual neural networks for histopathological malignancy diagnosis, where a case is defined as a sequence of images from the patient at all available levels of magnification. Effectively, through mimicking what a human expert would actually do, our approach makes a diagnosis decision based on features learned in combination at multiple magnification levels.

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DOI

10.5244/C.31.122
https://dx.doi.org/10.5244/C.31.122

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Bibtex

            @inproceedings{BMVC2017_122,
                title={Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks},
                author={Qicheng Lao and Thomas Fevens},
                year={2017},
                month={September},
                pages={122.1-122.11},
                articleno={122},
                numpages={11},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.122},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.122}
            }