BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation

要約

タイトル：Medial Image Segmentationに向けたBoundary-Aware Lightweight Transformer（BATFormer）への進展

要約：

– CNNの受容野の不適当さを補うために生まれたTransformerは、最近爆発的な関心を集めています。
– しかし、大域的な表現学習の難解な計算複雑性と、硬直したウィンドウ分割による、医用画像分割の展開を妨げています。
– この論文では、Transformerの上の医用画像分割のための2つの問題に取り組みます。
– 重点的に、CGTモジュールは、豊富なグローバル特徴をより低い計算複雑性で提供するために複数の小規模特徴マップを共同で利用するための、クロススケールグローバルTransformerモジュールを導入します。
– 形状モデリングの重要性が医用画像分割で認められているため、BLTモジュールは構成されます。
– BATFormerは、最少のモデルパラメータと最も低い計算複雑性を必要とし、最新のアプローチと比較して全体的なDiceが92.84％、右室が91.97％、心筋が90.26％、左室が96.30％、またISIC 2018データセットではDice、IoU、ACCがそれぞれ90.76％、84.64％、96.76％の最高のパフォーマンスを示します。
– 結論として、BATFormerは、高速で効果的な医用画像セグメンテーションにおいて、カスタマイズされたTransformerの開発の必要性を示しています。

要約(オリジナル)

Objective: Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global representation learning, together with rigid window partitioning, hinders their deployment in medical image segmentation. This work aims to address the above two issues in transformers for better medical image segmentation. Methods: We propose a boundary-aware lightweight transformer (BATFormer) that can build cross-scale global interaction with lower computational complexity and generate windows flexibly under the guidance of entropy. Specifically, to fully explore the benefits of transformers in long-range dependency establishment, a cross-scale global transformer (CGT) module is introduced to jointly utilize multiple small-scale feature maps for richer global features with lower computational complexity. Given the importance of shape modeling in medical image segmentation, a boundary-aware local transformer (BLT) module is constructed. Different from rigid window partitioning in vanilla transformers which would produce boundary distortion, BLT adopts an adaptive window partitioning scheme under the guidance of entropy for both computational complexity reduction and shape preservation. Results: BATFormer achieves the best performance in Dice of 92.84%, 91.97%, 90.26%, and 96.30% for the average, right ventricle, myocardium, and left ventricle respectively on the ACDC dataset and the best performance in Dice, IoU, and ACC of 90.76%, 84.64%, and 96.76% respectively on the ISIC 2018 dataset. More importantly, BATFormer requires the least amount of model parameters and the lowest computational complexity compared to the state-of-the-art approaches. Conclusion and Significance: Our results demonstrate the necessity of developing customized transformers for efficient and better medical image segmentation.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI