LCE: A Framework for Explainability of DNNs for Ultrasound Image Based on Concept Discovery

TL;DR

The paper introduces LCE, a novel framework combining attribution and concept-based methods, utilizing SAM for meaningful ultrasound image explanations, and proposes a new faithfulness metric validated on breast ultrasound datasets.

Contribution

LCE is the first framework to integrate attribution with concept discovery for ultrasound DNN explanations, using SAM and a new faithfulness metric.

Findings

LCE outperforms common explainability methods on ultrasound datasets.

The new faithfulness metric provides more reliable evaluation.

LCE offers consistent explanations for detailed diagnostic tasks.

Abstract

Explaining the decisions of Deep Neural Networks (DNNs) for medical images has become increasingly important. Existing attribution methods have difficulty explaining the meaning of pixels while existing concept-based methods are limited by additional annotations or specific model structures that are difficult to apply to ultrasound images. In this paper, we propose the Lesion Concept Explainer (LCE) framework, which combines attribution methods with concept-based methods. We introduce the Segment Anything Model (SAM), fine-tuned on a large number of medical images, for concept discovery to enable a meaningful explanation of ultrasound image DNNs. The proposed framework is evaluated in terms of both faithfulness and understandability. We point out deficiencies in the popular faithfulness evaluation metrics and propose a new evaluation metric. Our evaluation of public and private breast ultrasound datasets (BUSI and FG-US-B) shows that LCE performs well compared to commonly-used explainability methods. Finally, we also validate that LCE can consistently provide reliable explanations for more meaningful fine-grained diagnostic tasks in breast ultrasound.