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Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes.

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Impact of Low Class Prevalence on the Performance Evaluation of Neural Network Based Classifiers: Experimental Study in the Context of Computer-Assisted Medical Diagnosis., , , and . IJCNN, page 2005-2009. IEEE, (2007)Domain Generalization for Medical Image Analysis: A Survey., , , , and . CoRR, (2023)Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images., and . MIDL, volume 227 of Proceedings of Machine Learning Research, page 829-845. PMLR, (2023)Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 1057507. SPIE, (2018)A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 7260 of SPIE Proceedings, page 72600F. SPIE, (2009)Perception-driven IT-CADe analysis for the detection of masses in screening mammography: initial investigation., , and . Medical Imaging: Computer-Aided Diagnosis, volume 7624 of SPIE Proceedings, page 762406. SPIE, (2010)A systematic study of the class imbalance problem in convolutional neural networks, , and . Neural Networks, (2018)Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics., , , and . IEEE Trans. Med. Imaging, 38 (2): 435-447 (2019)Convolutional neural networks rarely learn shape for semantic segmentation., and . Pattern Recognit., (February 2024)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)