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Article overview
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Which images to label for few-shot medical landmark detection? | | Quan Quan
; Qingsong Yao
; Jun Li
; S. Kevin Zhou
; | | Date: |
7 Dec 2021 | | Abstract: | The success of deep learning methods relies on the availability of
well-labeled large-scale datasets. However, for medical images, annotating such
abundant training data often requires experienced radiologists and consumes
their limited time. Few-shot learning is developed to alleviate this burden,
which achieves competitive performances with only several labeled data.
However, a crucial yet previously overlooked problem in few-shot learning is
about the selection of template images for annotation before learning, which
affects the final performance. We herein propose a novel Sample Choosing Policy
(SCP) to select "the most worthy" images for annotation, in the context of
few-shot medical landmark detection. SCP consists of three parts: 1)
Self-supervised training for building a pre-trained deep model to extract
features from radiological images, 2) Key Point Proposal for localizing
informative patches, and 3) Representative Score Estimation for searching the
most representative samples or templates. The advantage of SCP is demonstrated
by various experiments on three widely-used public datasets. For one-shot
medical landmark detection, its use reduces the mean radial errors on
Cephalometric and HandXray datasets by 14.2% (from 3.595mm to 3.083mm) and
35.5% (4.114mm to 2.653mm), respectively. | | Source: | arXiv, 2112.04386 | | Services: | Forum | Review | PDF | Favorites | |
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