Barnes & Noble Machine Learning Medical Imaging: 14th International Workshop, MLMI 2023, Held Conjunction with MICCAI Vancouver, BC, Canada, October 8, Proceedings, Part II at Hamilton Place in Chattanooga, TN
Machine Learning Medical Imaging: 14th International Workshop, MLMI 2023, Held Conjunction with MICCAI Vancouver, BC, Canada, October 8, Proceedings, Part II
Machine Learning Medical Imaging: 14th International Workshop, MLMI 2023, Held Conjunction with MICCAI Vancouver, BC, Canada, October 8, Proceedings, Part II in Chattanooga, TN
Machine Learning Medical Imaging: 14th International Workshop, MLMI 2023, Held Conjunction with MICCAI Vancouver, BC, Canada, October 8, Proceedings, Part II in Chattanooga, TN
The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus on major trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus on major trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
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