FHIST: A Benchmark for Few-shot Classification of Histological Images
Few-shot learning has recently attracted wide interest in image classification, but almost all the current public benchmarks are focused on natural images. The few-shot paradigm is highly relevant in medical-imaging applications due to the scarcity of labeled data, as annotations are expensive and r...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
31.05.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Few-shot learning has recently attracted wide interest in image
classification, but almost all the current public benchmarks are focused on
natural images. The few-shot paradigm is highly relevant in medical-imaging
applications due to the scarcity of labeled data, as annotations are expensive
and require specialized expertise. However, in medical imaging, few-shot
learning research is sparse, limited to private data sets and is at its early
stage. In particular, the few-shot setting is of high interest in histology due
to the diversity and fine granularity of cancer related tissue classification
tasks, and the variety of data-preparation techniques. This paper introduces a
highly diversified public benchmark, gathered from various public datasets, for
few-shot histology data classification. We build few-shot tasks and
base-training data with various tissue types, different levels of domain shifts
stemming from various cancer sites, and different class-granularity levels,
thereby reflecting realistic scenarios. We evaluate the performances of
state-of-the-art few-shot learning methods on our benchmark, and observe that
simple fine-tuning and regularization methods achieve better results than the
popular meta-learning and episodic-training paradigm. Furthermore, we introduce
three scenarios based on the domain shifts between the source and target
histology data: near-domain, middle-domain and out-domain. Our experiments
display the potential of few-shot learning in histology classification, with
state-of-art few shot learning methods approaching the supervised-learning
baselines in the near-domain setting. In our out-domain setting, for 5-way
5-shot, the best performing method reaches 60% accuracy. We believe that our
work could help in building realistic evaluations and fair comparisons of
few-shot learning methods and will further encourage research in the few-shot
paradigm. |
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DOI: | 10.48550/arxiv.2206.00092 |