An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation

Ahmed Abdulaal, Hugo Laurence Fry, Nina Montaña-Brown, Ayodeji Ijishakin, Jack Gao, Stephanie Hyland, Daniel C. Alexander, Daniel C. Castro

Keywords: Radiology, Mechanistic Interpretability, Medical Imaging, Sparse Autoencoders

Abstract: Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer from hallucinations, lack interpretability, and require expensive fine-tuning. Sparse Autoencoders (SAEs) have been shown to provide a principled approach to reverse-engineer a model's internal activations into discrete, verifiable components. Thus, we introduce SAE-Rad, the first instance of using mechanistic interpretability techniques explicitly for a downstream multi-modal reasoning task. SAE-Rad uses a novel SAE architecture to decompose latent representations from a pre-trained vision transformer into human-interpretable features. These features are then labelled using an off-the-shelf language model and compiled into a full report for each image, eliminating the need for fine-tuning large models for this task. On the MIMIC-CXR dataset, SAE-Rad achieves competitive radiology-specific metrics compared to state-of-the-art models while using significantly fewer computational resources for training. Qualitative analysis reveals that SAE-Rad learns meaningful visual concepts and generates reports aligning closely with expert interpretations. Our results suggest that SAEs can enhance multimodal reasoning in healthcare, providing a more interpretable alternative to existing VLMs.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 10926