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Predicting rheumatoid arthritis (RA) in an early-stage based on MRI can help initiate timely treatment and therefore halt the progression of the disease and increase the possibility of recovery. Deep learning methods are in general highly suitable for this type of labeling tasks. However, applying this approach to RA detection faces challenges from the lack of a large number of samples, difficulty in distinguishing patterns of RA from imaging artifacts, and a wide anatomical variation, leading to the failure of transfer learning based on pre-trained models. In this paper, a pre- and post-training method for this fewshot task is proposed. Based on the clinical MRI data, this method was validated through cross-validation, achieving a significant improvement in AUC, F1 score, and accuracy to the baseline deep learning models. Since these pre- and post-training strategies are intuitive, effective and easy to implement, they can also contribute to other challenging few-shot medical tasks.
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Friday 8th July
Poster Session 3.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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