Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clin
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- Artikel-Nr.: 10395848
Beschreibung
Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019).- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification.- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics.- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis.- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection.- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules.- Deep neural network or dermatologist?.- Towards Interpretability of Segmentation Networks by analyzing DeepDreams.- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019).- Towards Automatic Diagnosis from Multi-modal Medical Data .- Deep Learning based Multi-Modal Registration for Retinal Imaging .- Automated Enriched Medical Concept Generation for Chest X-ray Images .
Eigenschaften
Breite: | 157 |
Gewicht: | 184 g |
Höhe: | 235 |
Länge: | 8 |
Seiten: | 93 |
Sprachen: | Englisch |
Autor: | Anant Madabhushi, Ben Glocker, Ender Konukoglu, Hayit Greenspan, Kenji Suzuki, Mauricio Reyes, Roland Wiest, Tanveer Syeda-Mahmood, Yaniv Gur |
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