Advanced image analysis can lead to automated examination to histopatholgy images which is essential for ob- jective and fast cancer diagnosis. Recently deep learning methods, in particular Convolutional Neural Networks (CNNs), have shown exceptionally successful performance on medical image analysis as well as computational histopathology. Because Whole-Slide Images (WSIs) have a very large size, the CNN models are commonly applied to classify WSIs per patch. Although a CNN is trained on a large part of the input space, the spatial dependencies between patches are ignored and the inference is performed only on appearance of the individual patches. Therefore, prediction on the neighboring regions can be inconsistent. In this paper, we apply Con- ditional Random Fields (CRFs) over latent spaces of a trained deep CNN in order to jointly assign labels to the patches. In our approach, extracted compact features from intermediate layers of a CNN are considered as observations in a fully-connected CRF model. This leads to performing inference on a wider context rather than appearance of individual patches. Experiments show an improvement of approximately 3.9% on average FROC score for tumorous region detection in histopathology WSIs. Our proposed model, trained on the Camelyon171 ISBI challenge dataset, won the 2nd place with a kappa score of 0.8759 in patient-level pathologic lymph node classification for breast cancer detection.
Farhad Ghazvinian Zanjani, Svitlana Zinger, and Peter H. N. de With, "Cancer detection in histopathology whole-slide images using conditional random fields on deep embedded spaces," Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810I (Presented at SPIE Medical Imaging: February 12, 2018; Published: 6 March 2018); https://doi.org/10.1117/12.2293107.
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