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UnknownDeep learning model calibration for improving performance in class-imbalanced medical image classification tasks.
In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge, therefore biasing the predicted class probabilit...
S-EPMC8794113 | biostudies-literature
UnknownDeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.
Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and the clavicles. Automated bone suppression methods would increase soft tissue visibility and enhance automated disease detection. We evaluate this hyp...
S-EPMC8970404 | biostudies-literature
UnknownNovel loss functions for ensemble-based medical image classification.
Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train deep neural netw...
S-EPMC8718001 | biostudies-literature
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