Few-shot Medical Image Classification with MAML Based on Dice Loss

In medical image processing based on deep learning, medical image datasets are usually few-shot and unbalanced between classes. This will cause the model to overfit and be biased towards the class with more samples during training and the accuracy of the class with fewer samples will decrease. As a...

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Veröffentlicht in:	2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) S. 348 - 351
Hauptverfasser:	Zhang, Ce, Cui, Qingshan, Ren, Shaolong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 28.10.2022
Schlagworte:	Biomedical image processing Costs Deep learning dice loss Entropy few-shot learning imbalanced learning Linear programming Machine learning model-agnostic meta-learning Neural networks Training
Online-Zugang:	Volltext
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