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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)

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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)XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans., , , , , , , , , and 3 other author(s). CoRR, (2024)Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques., , , and . CoRR, (2019)Classification of chest CT using case-level weak supervision., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 1095017. SPIE, (2019)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)Quantification of Trabeculae Inside the Heart from MRI Using Fractal Analysis., and . CoRR, (2018)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)Attention-guided classification of abnormalities in semi-structured computed tomography reports., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)