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What limits performance of weakly supervised deep learning for chest CT classification?, , , and . CoRR, (2024)Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations., , and . CoRR, (2019)Automatic Skin Lesion Segmentation Using GrabCut in HSV Colour Space.. CoRR, (2018)Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)Data diversity and virtual imaging in AI-based diagnosis: A case study based on COVID-19., , , , , , and . CoRR, (2023)Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans., , , , , , , and . CoRR, (2020)Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning., , , , , , and . BMC Medical Informatics Decis. Mak., 22 (1): 102 (2022)Attention-guided classification of abnormalities in semi-structured computed tomography reports., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)