Quantifying interpretation reproducibility in Vision Transformer models with TAVAC | JoVE Visualize

Area of Science:

Artificial Intelligence
Digital Pathology
Medical Imaging Analysis

Background:

Deep learning, particularly Vision Transformer (ViT) models, shows promise for extracting diagnostic features from biomedical images, outperforming traditional CNNs in spatial relationship capture and interpretability.
However, limited annotated datasets can lead to ViT model overfitting, resulting in inaccurate predictions due to noise, hindering clinical application.
Ensuring the reliability and reproducibility of AI model interpretations is crucial for both clinical diagnostics and basic scientific research.

Purpose of the Study:

To introduce Training Attention and Validation Attention Consistency (TAVAC), a novel metric for evaluating Vision Transformer (ViT) model overfitting in biomedical image analysis.
To quantify the reproducibility of interpretations generated by ViT models, ensuring reliable feature extraction.
To differentiate between on-target and off-target attention mechanisms within ViT models.

Main Methods:

TAVAC was developed by comparing high-attention regions between training and testing phases of ViT models.
The metric was validated on four public image classification datasets and two independent breast cancer histological image datasets.
TAVAC's ability to distinguish between on-target and off-target attentions and measure interpretation generalization at a cellular level was assessed.

Main Results:

Overfitted ViT models consistently exhibited significantly lower TAVAC scores compared to well-generalized models.
TAVAC effectively distinguished between relevant (on-target) and irrelevant (off-target) attention patterns within the models.
The metric demonstrated capability in measuring interpretation generalization at a fine-grained cellular level, applicable to both biomedical and general images.

Conclusions:

TAVAC is a robust metric for evaluating ViT model overfitting and enhancing interpretative reproducibility in digital pathology and beyond.
The metric aids in identifying unreliable predictions stemming from overfitting, thereby improving the trustworthiness of AI in medical diagnostics.
TAVAC's application extends to basic research, facilitating the discovery of critical spatial patterns and cellular structures through reliable interpretation of imaging data.