Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling

要約

タイトル：自己教師あり学習とバッチ知識アンサンブルによるCOVID-19検出性能の向上

要約：
– 問題：胸部X線画像（CXR）からCOVID-19を検出することは、COVID-19を検出する最も速く簡単な方法の1つになっています。しかし、既存の手法は、通常、自然画像からの教師あり転移学習をプレトレーニングプロセスとして使用しています。これらの手法はCOVID-19のユニークな特徴や他の肺炎との類似した特徴を考慮していません。
– 目的：本論文では、COVID-19のユニークな特徴や他の肺炎との類似した特徴を考慮した、高精度のCOVID-19検出方法を設計したいと考えています。
– 方法：当社の手法は2つのフェーズから構成されています。1つは自己教師あり学習ベースのプレトレーニング、もう1つはバッチ知識アンサンブルベースのファインチューニングです。
– 結果：公開された2つのCOVID-19 CXRデータセット、すなわち大規模なデータセットと不均衡なデータセットに対して、当社の手法は有望なCOVID-19検出性能を発揮しました。

要約(オリジナル)

Problem: Detecting COVID-19 from chest X-Ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. Aim: In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. Methods: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. Results: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI