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Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those.
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size
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UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support
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Guideline-Based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support
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Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support
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Ophthalmic diagnosis using deep learning with fundus images–A critical review
Sengupta, S.; Singh, A.; Leopold, H.A.; Gulati, T.; Lakshminarayanan, V · 2020
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Leopold, H.; Singh, A.; Sengupta, S.; Zelek, J.; Lakshminarayanan, V., Recent Advances in Deep Learning Applications for Retinal Diagnosis using OCT · 2020
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Explainable 3D convolutional neural network using GMM encoding
Stano, M.; Benesova, W.; Martak, L.S · 2020
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Meyes, R.; de Puiseau, C.W.; Posada-Moreno, A.; Meisen, T · 2020
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R.; others · 2020
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Interpretability of machine learning based prediction models in healthcare
Stiglic, G.; Kocbek, P.; Fijacko, N.; Zitnik, M.; Verbert, K.; Cilar, L · 2020
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Interpretation of deep learning using attributions : application to ophthalmic diagnosis
Singh, A.; Sengupta, S.; Lakshminarayanan, V · 2020
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Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI
Papanastasopoulos, Z.; Samala, R.K.; Chan, H.P.; Hadjiiski, L.; Paramagul, C.; Helvie, M.A.; Neal, C.H · 2020
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Wang, L.; Wong, A · 2020
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Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
Wickstrøm, K.; Kampffmeyer, M.; Jenssen, R · 2020
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Classification of brain lesions from MRI images using a novel neural network
Bamba, U.; Pandey, D.; Lakshminarayanan, V · 2020
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SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation
Sun, J.; Darbeha, F.; Zaidi, M.; Wang, B · 2020
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Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
Biffi, C.; Cerrolaza, J.J.; Tarroni, G.; Bai, W.; De Marvao, A.e.a · 2020
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Eslami, T.; Raiker, J.S.; Saeed, F · 2020
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