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On the Challenges and Perspectives of Foundation Models for Medical Image Analysis
Shaoting Zhang, Dimitris Metaxas
TL;DR
Medical image analysis must address diverse modalities and scales, expensive annotations, and variability across clinical data. This article presents a spectrum of foundation models and discusses their uses, adaptation strategies, data requirements, and integration with language models. It concludes that collaboration and open-source families of pretrained models spanning imaging modalities are needed.
Problem
Medical foundation-model development is constrained by diverse imaging modalities and scales, limited high-quality annotations, and substantial variation in organs, structures, and imaging data.
Method
The article surveys a spectrum of general vision, modality-specific, organ-specific, and task-specific foundation models and discusses their medical applications and adaptation strategies.
Results
The article identifies potential benefits including reduced labeled-data requirements, more reliable diagnoses and treatment decisions, and integration of medical images with language models for reporting and decision support.
Takeaways & Limitations
Researchers should explore the foundation-model spectrum and collaborate across institutions and disciplines to release pretrained model families for varied imaging modalities.
Takeaways & Limitations
Unified foundation models often cannot achieve state-of-the-art performance across medical image-analysis tasks because organs, structures, textures, shapes, topologies, and modalities vary substantially.
Abstract
from arXiv · showhide
This article discusses the opportunities, applications and future directions of large-scale pre-trained models, i.e., foundation models, for analyzing medical images. Medical foundation models have immense potential in solving a wide range of downstream tasks, as they can help to accelerate the development of accurate and robust models, reduce the large amounts of required labeled data, preserve the privacy and confidentiality of patient data. Specifically, we illustrate the "spectrum" of medical foundation models, ranging from general vision models, modality-specific models, to organ/task-specific models, highlighting their challenges, opportunities and applications. We also discuss how foundation models can be leveraged in downstream medical tasks to enhance the accuracy and efficiency of medical image analysis, leading to more precise diagnosis and treatment decisions.
1 Introduction
Foundation models differ from traditional pretrained models by supporting few-shot, zero-shot, or prompt-based use across tasks, but medical imaging’s modality and scale diversity complicate unified model development. The paper presents a spectrum from general vision models to modality-, organ-, and task-specific models.
- Foundation models can address varied tasks with one set of weights through few-shot learning, zero-shot learning, or prompt engineering.Traditional pretrained models typically require extensive supervised fine-tuning for specific downstream tasks.
- Medical image analysis still relies mainly on task-specific models, while medical foundation models must accommodate diverse modalities, physics, energy sources, and image scales.Medical images range from molecules and cells to organ systems and the full body.
- The paper presents a spectrum of medical foundation models spanning general vision, modality-specific, organ-specific, and task-specific models.It proposes this spectrum as a framework for investigating foundation-model uses in medical image analysis.
2 The Spectrum of Foundation Models
The paper organizes medical foundation models along a spectrum from general vision systems to modality-, organ-, and task-specific models. This spectrum reflects a trade-off between broad reuse and the specialized representations, accuracy, interpretability, and efficiency needed for heterogeneous clinical images and tasks.
- Vision Foundation Models: Natural-image vision foundation models provide a starting point for medical applications, but adapting them requires fine-tuning, adapters, prompting, or specialized architectures.Medical image features and patterns differ substantially from those in natural images.
- Vision Foundation Models: SAM shows strong zero-shot generalization on natural images, yet its out-of-the-box performance is unsatisfactory for complex medical segmentation tasks.Further tuning, adapters, or effective prompts can improve performance using downstream data and annotations.
- Vision Foundation Models: Unified foundation models often cannot achieve state-of-the-art performance because medical organs, structures, textures, shapes, sizes, topologies, and modalities vary substantially.Parameter- and data-efficient tuning methods can adapt vision foundation models to long-tail medical data.
- Modality-specific Foundation Models: Modality-specific models learn features relevant to particular modalities, such as bone density and tissue contrast in CT or soft-tissue contrast and motion in MRI.They may improve accuracy and efficiency for modality-specific tasks but generalize poorly across modalities.
- Organ/Task-specific Foundation Models: Organ- and task-specific models target variability in organ appearance and diverse clinical tasks, potentially improving accuracy and interpretability while reducing labeled-data needs for new tasks.Their training remains challenging because they require large amounts of labeled data.
- General vs. Specialized Foundation Models: General systems support multiple tasks and modalities with one set of weights, whereas specialized systems focus on discrete clinical tasks, organs, or modalities.General systems consume significantly more computational resources and often lack the required accuracy, motivating exploration of trade-offs between development effort and practical efficacy.
3 Data Requirements for Foundation Models
Medical foundation models depend on costly, heterogeneous data whose acquisition, annotation, interpretation, and quality vary across settings. The paper therefore discusses modality-specific dataset construction and notes that efficient adaptation and emerging benchmarks may support broader deployment, while model openness remains uneven.
- Data to Pretrain the Foundation Models: Medical data are expensive to collect, annotate, and interpret, and their quality varies substantially across hospitals and clinical studies.The paper identifies data as the cornerstone of training foundation models.
- Data to Pretrain the Foundation Models: Medical images are acquired for specific clinical purposes using protocols and scanners controlled by experts, unlike the broad variability readily captured in many real-world image datasets.These acquisition conditions contribute to medical-image complexity and variability.
- Data to Pretrain the Foundation Models: Classical public medical datasets are usually limited to one modality, anatomical area, and task, while newer datasets combine multiple protocols and scanners.The paper contrasts purpose-specific datasets with newer general-purpose collections.
- Data to Pretrain the Foundation Models: A feasible data strategy is to begin with modality-specific datasets and merge complementary datasets rather than immediately building one unified medical image database.The paper compares this approach with an “ImageNet”-type database for medical data.
- Data to Pretrain the Foundation Models: Efficient adaptation methods can support downstream foundation-model training with only a minimal number of samples, including prompt engineering and efficient retraining.
- Data to Pretrain the Foundation Models: Benchmarks and released datasets are being established to support deployment of large-scale foundation models across clinical challenges.
4 Applications and Benefits of Foundation Models
Foundation models can support diverse medical image-analysis tasks while reducing annotation demands, addressing long-tail cases, and enabling privacy-preserving knowledge sharing. Their integration with language models also opens opportunities for automated reporting and clinical decision support, although explainability and generalizability remain important challenges.
- Applications and benefits: Few-shot learning can help address long-tailed medical datasets where rare disease cases are scarce and model performance may degrade.The approach aligns with settings in which only a few examples of rare diseases are available.
- Applications and benefits: Large-scale medical foundation models can reduce labeled-data and manual-annotation requirements while supporting more reliable diagnosis and treatment decisions.This is especially valuable because medical professionals must otherwise repeatedly hand-label image data.
- Explainable and generalizable models: Clinical deployment still requires improved explainability and generalizability because clinicians need interpretable inferences and models must handle domain shifts across medical centers.These concerns reflect dataset limitations and variation between institutions.
- Applications and benefits: Foundation models provide a unified framework for detection, segmentation, and classification across medical image-analysis tasks.Training across medical centers, scanners, and protocols can produce broadly generalizable feature representations.
- Privacy-preserving learning: Transfer learning, federated learning, and synthetic-image generation offer ways to share or adapt foundation-model knowledge while protecting patient privacy.Federated learning keeps distributed data on local machines, while synthetic images can substitute statistically similar real patient images for training.
- Integration with large language models: Vision-language models can connect medical images with text to generate captions, automate radiology reports, and support diagnosis and prognosis.Current frameworks are still preliminary because they commonly prompt existing language models without fully fine-tuning or consolidating modalities.
5 Future Directions of Medical Foundation Models
Future work centers on collaborative, open-source development of a spectrum of medical foundation models spanning imaging modalities and broader data types and scales. These multimodal models could support clinical workflows such as radiology reporting and real-time surgical decision-making.
- Collaboration and open source: Researchers are encouraged to collaborate across institutions and disciplines and release pretrained models covering various imaging modalities.The paper frames this as a way to investigate the spectrum of medical foundation models and improve their design.
- Multi-modality foundation models: Future multi-modality foundation models could combine text, images, video, databases, and molecules across scales from genes and cells to patients and populations.The goal is a more comprehensive understanding and assessment of a patient’s condition.
- Clinical use cases: Radiology applications could draft structured reports and describe abnormalities, diagnoses, prognoses, and proposed treatments.These models are presented as workflow-support tools for radiologists.
- Clinical use cases: Integrating image, language, and audio modalities could help surgeons make real-time decisions during operations.This is identified as another possible clinical use case for advances in multimodal foundation models.