A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

TL;DR

The paper introduces the Tool Bottleneck Framework (TBF), a novel approach for medical image understanding that combines vision-language models with a learned tool composition method, improving interpretability and performance especially with limited data.

Contribution

It proposes a new framework that uses a learned model to compose tools selected by vision-language models, enhancing interpretability and effectiveness in medical imaging tasks.

Findings

TBF performs on par or better than existing methods in histopathology and dermatology tasks.

The framework shows particular advantages in data-limited scenarios.

TBF improves interpretability of medical image predictions.

Abstract

Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools. Broadly, these frameworks leverage VLMs and a pre-specified toolbox to decompose the prediction task into multiple tool calls (often deep learning models) which are composed to make a prediction. The dominant approach to composing tools is using text, via function calls embedded in VLM-generated code or natural language. However, these methods often perform poorly on medical image understanding, where salient information is encoded as spatially-localized features that are difficult to compose or fuse via text alone. To address this, we propose a tool-use framework for medical image understanding called the Tool Bottleneck Framework (TBF), which composes VLM-selected tools using a learned Tool Bottleneck Model (TBM). For a given image and task, TBF leverages an off-the-shelf medical VLM to select tools from a toolbox that each extract clinically-relevant features. Instead of text-based composition, these tools are composed by the TBM, which computes and fuses the tool outputs using a neural network before outputting the final prediction. We propose a simple and effective strategy for TBMs to make predictions with any arbitrary VLM tool selection. Overall, our framework not only improves tool-use in medical imaging contexts, but also yields more interpretable, clinically-grounded predictors. We evaluate TBF on tasks in histopathology and dermatology and find that these advantages enable our framework to perform on par with or better than deep learning-based classifiers, VLMs, and state-of-the-art tool-use frameworks, with particular gains in data-limited regimes. Our code is available at https://github.com/christinaliu2020/tool-bottleneck-framework.