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The inability to interpret the model prediction in semantically and visuallymeaningful ways is a well-known shortcoming of most existing computer-aideddiagnosis methods. In this paper, we propose MDNet to establish a directmultimodal mapping between medical images and diagnostic reports that can readimages, generate diagnostic reports, retrieve images by symptom descriptions,and visualize attention, to provide justifications of the network diagnosisprocess. MDNet includes an image model and a language model. The image model isproposed to enhance multi-scale feature ensembles and utilization efficiency.The language model, integrated with our improved attention mechanism, aims toread and explore discriminative image feature descriptions from reports tolearn a direct mapping from sentence words to image pixels. The overall networkis trained end-to-end by using our developed optimization strategy. Based on apathology bladder cancer images and its diagnostic reports (BCIDR) dataset, weconduct sufficient experiments to demonstrate that MDNet outperformscomparative baselines. The proposed image model obtains state-of-the-artperformance on two CIFAR datasets as well.

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network