Titlebook: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clin; Second International Kenji Suzuki,Mau

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Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-A of its outstanding performance. Interpretability can guarantee the confidence of the deep learning system, therefore it is particularly important in the medical field. In this study, a novel deep network is proposed to explain the diagnostic decision with visual pointing map and diagnostic sentence

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Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detectionomatic estimation of MLS was recently highlighted by ACR Data Science Institute. In this paper we introduce a novel deep learning based approach for the problem of MLS detection, which exploits task-specific structural knowledge. We evaluate our method on a large dataset containing heterogeneous ima

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Deep Neural Network or Dermatologist?constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two ex

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Correction to: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal L