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Toward cognitive pipelines of medical assistance algorithms.

, , , , , , , , , , , , , и . Int. J. Comput. Assist. Radiol. Surg., 11 (9): 1743-1753 (2016)

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Michael Götz

Michael Götz

Michael Götz

Michael Götz

Michael Götz

 

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RPTK: The Role of Feature Computation on Prediction Performance., , , , , , , , , и 4 other автор(ы). MTSAIL/LEAF/AI4Treat/MMMI/REMIA@MICCAI, том 14394 из Lecture Notes in Computer Science, стр. 113-122. Springer, (2023)DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images., , , , , , , , , и . CoRR, (2024)Abstract: Deep-learning on Lossily Compressed Pathology Images - Adverse Effects for ImageNet Pre-trained Models., , , , , , , , , и 1 other автор(ы). Bildverarbeitung für die Medizin, стр. 245. Springer, (2023)On Leakage in Machine Learning Pipelines., , , , , , , , , и 2 other автор(ы). CoRR, (2023)Dealing with Small Datasets for Deep Learning in Medical Imaging: An Evaluation of Self-Supervised Pre-Training on CT Scans Comparing Contrastive and Masked Autoencoder Methods for Convolutional Models., , , , , , и . CoRR, (2023)Abstract: Interpretable Medical Image Classification Using Prototype Learning and Privileged Information., , и . Bildverarbeitung für die Medizin, стр. 25. Springer, (2024)Abstract: Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning., , , , , , , , , и 2 other автор(ы). Bildverarbeitung für die Medizin, стр. 158. Springer, (2024)Abstract: Data Augmentation for Information Transfer - Why Controlling for Confounding Effects in Radiomic Studies is Important and How to do it., и . Bildverarbeitung für die Medizin, стр. 140. Springer, (2021)Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation., , , и . CoRR, (2024)Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models., , , , , , , , , и 1 other автор(ы). MOVI@MICCAI, том 13578 из Lecture Notes in Computer Science, стр. 73-83. Springer, (2022)