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15 March 2019 Predicting histopathological findings of gastric cancer via deep generalized multi-instance learning
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Abstract
In this paper, we investigate the problem of predicting the histopathological findings of gastric cancer (GC) from preoperative CT image. Unlike most existing classification systems assess the global imaging phenotype of tissues directly, we formulate the problem as a generalized multi-instance learning (GMIL) task and design a deep GMIL framework to address it. Specifically, the proposed framework aims at training a powerful convolutional neural network (CNN) which is able to discriminate the informative patches from the neighbor confusing patches and yield accurate patient-level classification. To achieve this, we firstly train a CNN for coarse patch-level classification in a GMIL manner to develop several groups which contain the informative patches for each histopathological category, the intra-tumor ambiguous patches, and the extra-tumor irrelative patches respectively. Then we modify the fully-connected layer to introduce the latter two classes of patches and retrain the CNN model. In the inference stage, patient-level classification is implemented based on the group of candidate informative patches automatically recognized by the model. To evaluate the performance and generalizability of our approach, we successively apply it to predict two kinds of histopathological findings (differentiation degree [two categories] and Lauren classification [three categories]) on a dataset including 433 GC patients with venous phase contrast-enhanced CT scans. Experimental results reveal that our deep GMIL model has a powerful predictive ability with accuracies of 0.815 and 0.731 in the two applications respectively, and it significantly outperforms the standard CNN model and the traditional texture-based model (more than 14% and 17% accuracy increase).
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Mengjie Fang, Wenjuan Zhang, Di Dong, Junlin Zhou, and Jie Tian "Predicting histopathological findings of gastric cancer via deep generalized multi-instance learning", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491Q (15 March 2019); https://doi.org/10.1117/12.2512435
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