Download PDFOpen PDF in browserMiniDigPath: a New Standard for Pathology Images Few-Shot Learning ClassificationEasyChair Preprint 96898 pages•Date: February 9, 2023AbstractThe miniDigPath dataset, which composed by 6 public pathology image datasets, is proposed by our work for few-shot learning (FSL). It consists 67 different diseases and tissue types, and every type has 48-500 tissue image blocks. In total, there are 21165 histopathology images. Importantly, miniDigPath is available publicly for every researcher. It explores a new idea to solve pathological images annotation using FSL, which is the importance and originality of the dataset we proposed. Experimental evaluation on the classical FSL algorithm and our method shows that the miniDigPath dataset can accomplish the task of FSL. Besides, FSL has good advantage for classification of digital pathology images. Keyphrases: digital pathology images, few-shot learning, miniDigPath
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