Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis

TL;DR

This paper introduces SMARTFormer, a novel masked image modeling approach for medical images that uses semantic attention and a noisy teacher to improve transformer pretraining and downstream task accuracy, especially with limited data.

Contribution

The paper develops SMARTFormer, a hierarchical Swin transformer with semantic attention and a noisy teacher, enabling effective masked image modeling for 3D medical images.

Findings

Achieved 89.5% accuracy in classifying lung nodules.

Predicted lung cancer treatment response with 74% accuracy.

Improved unsupervised segmentation of organs and tumors.

Abstract

Pretraining vision transformers (ViT) with attention guided masked image modeling (MIM) has shown to increase downstream accuracy for natural image analysis. Hierarchical shifted window (Swin) transformer, often used in medical image analysis cannot use attention guided masking as it lacks an explicit [CLS] token, needed for computing attention maps for selective masking. We thus enhanced Swin with semantic class attention. We developed a co-distilled Swin transformer that combines a noisy momentum updated teacher to guide selective masking for MIM. Our approach called \textsc{s}e\textsc{m}antic \textsc{a}ttention guided co-distillation with noisy teacher \textsc{r}egularized Swin \textsc{T}rans\textsc{F}ormer (SMARTFormer) was applied for analyzing 3D computed tomography datasets with lung nodules and malignant lung cancers (LC). We also analyzed the impact of semantic attention and noisy teacher on pretraining and downstream accuracy. SMARTFormer classified lesions (malignant from benign) with a high accuracy of 0.895 of 1000 nodules, predicted LC treatment response with accuracy of 0.74, and achieved high accuracies even in limited data regimes. Pretraining with semantic attention and noisy teacher improved ability to distinguish semantically meaningful structures such as organs in a unsupervised clustering task and localize abnormal structures like tumors. Code, models will be made available through GitHub upon paper acceptance.