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ArXiv Preprint

Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge, in a 3-classes unbalanced classification task. We report cross-validation (resp. validation) balanced-accuracy, kappa and f1 of 0.913, 0.897 and 0.951 (resp. 0.91, 0.90 and 0.94). The complete code of the method is open-source at XXXX. Those include histopathological data pre-processing, and can therefore be used off-the-shelf for other histopathological and/or radiological classification.

Marvin Lerousseau, Eric Deutsh, Nikos Paragios

2020-09-03