A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis (2405.14839v2)

Abstract: While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images. A key finding we show empirically is that existing visual backbones lack an appropriate prior from the architecture for reliable generalization in these settings. Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language. To this end, we introduce Knowledge-enhanced Bottlenecks (KnoBo), a class of concept bottleneck models that incorporates knowledge priors that constrain it to reason with clinically relevant factors found in medical textbooks or PubMed. KnoBo uses retrieval-augmented LLMs to design an appropriate concept space paired with an automatic training procedure for recognizing the concept. We evaluate different resources of knowledge and recognition architectures on a broad range of domain shifts across 20 datasets. In our comprehensive evaluation with two imaging modalities, KnoBo outperforms fine-tuned models on confounded datasets by 32.4% on average. Finally, evaluations reveal that PubMed is a promising resource for making medical models less sensitive to domain shift, outperforming other resources on both diversity of information and final prediction performance.

Simplified and Effective Model for Medical Image Analysis with KnoBo

Understanding the Motivation

Medical imaging is a critical domain where robustness and reliability are paramount. However, deep learning models, which shine when applied to everyday, natural images, often stumble when faced with medical images. One major challenge is that medical datasets are small and prone to domain shifts—variations in data coming from different hospitals or patient demographics (like age, race, sex). This can dramatically hurt model performance, leading to a lack of trust among medical professionals.

The Proposal: Knowledge-enhanced Bottlenecks (KnoBo)

The central idea of the paper is that existing models lack appropriate prior knowledge for the medical domain. To tackle this, the researchers propose KnoBo (Knowledge-enhanced Bottlenecks), a new model design inspired by medical training processes. Here's a breakdown of how KnoBo works:

1. Capture Human Knowledge: KnoBo integrates explicit medical knowledge into its decision-making process. This is akin to how medical students first learn from textbooks before applying knowledge practically.
2. Concept Bottlenecks: KnoBo utilizes concept bottleneck models (CBMs), which make decisions based on human-readable concepts derived from trusted medical sources like textbooks and PubMed articles.
3. Automated Concept Generation: It employs retrieval-augmented LLMs to automatically identify relevant concepts from medical corpora.

Breaking Down the Model

Structural Prior

KnoBo uses LLMs like GPT-4 to query and retrieve relevant medical documents. These retrieved documents help construct a "concept structure," which forms the basis for the bottlenecks. Essentially, this step ensures that the concepts are grounded in real medical knowledge.

Bottleneck Predictor

Once the structure is set, the next step is to train grounding functions that map image features to probabilities of these predefined concepts. For example, concepts such as “ground-glass opacity” in X-rays or “skin color” in lesion images are identified, enhancing interpretability and reliability.

Parameter Prior

The model's decision-making parameters are aligned with known medical knowledge. This step involves regularizing parameters so they adhere to medically accurate associations, aiming to improve both interpretability and trustworthiness.

Key Findings

1. Robustness and Performance: KnoBo demonstrates substantial robustness to domain shifts. In evaluations across various medical image datasets, KnoBo consistently outperformed traditional fine-tuned models by significant margins. Notably, it improved out-of-domain (OOD) performance by 41.8% and 22.9% on X-ray and skin lesion datasets, respectively.
2. Interpretability: With KnoBo, each decision-making step is more transparent. This is crucial for high-stake domains like healthcare, where understanding the reasoning behind model predictions can greatly aid in medical decision-making.
3. Effectiveness of Knowledge Sources: Among different knowledge sources, PubMed emerged as the most effective. It provided a diverse and rich set of concepts that enhanced the model's performance.

Practical and Theoretical Implications

For Practitioners: KnoBo's approach can pave the way for more reliable AI systems in medical imaging, making them better suited for clinical adoption. Its use of explicit medical knowledge can also aid practitioners in understanding and trusting model predictions.

For Researchers: The paper opens avenues for enhancing model robustness using knowledge-augmented methods. Future research could explore extending these techniques to other medical specialties or even other critical domains where interpretability and robustness are crucial.

Future Directions

1. Refinement of Concept Grounding: As more advanced pretraining datasets and models become available, the process of grounding concepts in medical images could become more nuanced and accurate.
2. Extension to Other Modalities: KnoBo's principles could be applied to other areas in medical diagnostics, like MRI scans or ultrasound images.
3. Integration with Real-world Systems: Implementing KnoBo in real clinical settings would be a critical next step to evaluate its practical usability and impact.

Conclusion

The paper presents a novel yet straightforward way to improve the robustness and interpretability of medical image analysis models. KnoBo leverages well-established medical knowledge to guide its predictions, making it a valuable tool for both medical professionals and researchers alike. With its promising results and practical approach, KnoBo sets a standard for future advancements in medical AI.