A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta) (2404.16000v1)

Abstract: While the field of medical image analysis has undergone a transformative shift with the integration of machine learning techniques, the main challenge of these techniques is often the scarcity of large, diverse, and well-annotated datasets. Medical images vary in format, size, and other parameters and therefore require extensive preprocessing and standardization, for usage in machine learning. Addressing these challenges, we introduce the Medical Imaging Meta-Dataset (MedIMeta), a novel multi-domain, multi-task meta-dataset. MedIMeta contains 19 medical imaging datasets spanning 10 different domains and encompassing 54 distinct medical tasks, all of which are standardized to the same format and readily usable in PyTorch or other ML frameworks. We perform a technical validation of MedIMeta, demonstrating its utility through fully supervised and cross-domain few-shot learning baselines.

• The paper introduces MedIMeta, a comprehensive meta-dataset consolidating 19 datasets across 10 domains to advance cross-domain few-shot learning in medical imaging.
• It standardizes images to 224x224 pixels and provides pre-made training, validation, and test splits, simplifying dataset use for both single-task and multi-task setups.
• Experimental results demonstrate robust performance in both fully supervised and few-shot learning scenarios, underscoring its potential for advancing diagnostic algorithms.

Comprehensive Analysis of the Medical Imaging Meta-Dataset (MedIMeta): Facilitating Cross-Domain Few-Shot Learning

Introduction

In addressing the intricate challenges of medical image analysis, the necessity for extensive, diverse, and well-annotated image datasets is paramount to enhancing diagnostic algorithms via ML. This paper introduces MedIMeta, a large multi-domain, multi-task meta-dataset aimed at advancing the development and assessment of ML models. Particularly, MedIMeta is designed to facilitate the exploration and benchmarking of cross-domain few-shot learning (CD-FSL) algorithms in medical imaging contexts.

MedIMeta Dataset Overview

• MedIMeta amalgamates 19 distinct medical imaging datasets covering 10 different domains and includes 54 unique medical tasks.
• Tasks range from diagnostic categories to auxiliary ones like gender prediction, supporting both single-task and multi-task training frameworks.
• Standardized Image Size: All images are standardized to 224x224 pixels, aligning with common dimensions used in pre-trained models, thus obviating the need for additional preprocessing.
• Accessibility: Accompanied by a Python package to facilitate straightforward data loading and utilization within PyTorch, enhancing usability for ML research.
• Pre-made Data Splits: Promotes consistent benchmarking by providing predefined splits for training, validation, and test sets.

Comparative Context

Existing meta-datasets predominantly target non-medical applications with only a few incorporating medical images. MedIMeta uniquely provides a substantial number of medical tasks and supports multi-task learning setups within medical domains. This positions MedIMeta distinctively against other datasets like Meta-Dataset, VTAB, or MedMNIST v2, particularly in terms of domain variety and resolution quality.

Technical Validation

To authenticate the utility of MedIMeta, a series of experiments were carried out:

1. Fully Supervised Baseline: Models trained on individual tasks within MedIMeta demonstrated solid performance, affirming the dataset's quality and robustness.
2. Cross-Domain Few-Shot Learning (CD-FSL): Testing included CD-FSL techniques like ImageNet pre-training, multi-domain multi-task pre-training, and multi-domain multi-task MAML. Performance varied across tasks indicating diverse levels of complexity and difficulty inherent within and across these tasks.
3. Performance Assessment: The models achieved notable AUROC values across most tasks, with detailed performance metrics documented that aid in indicating challenging areas within the dataset worth further exploration.

Implications and Future Work

MedIMeta's extensive task variety and domain coverage not only allow for advanced algorithm development but also invite research into generalizable models capable of CD-FSL. The detailed validation provides a benchmark for subsequent models and highlights the dataset's potential to test and improve the efficacy of algorithms in real-world scenarios. Future advancements might involve the integration of additional medical domains or newer tasks that could extend the dataset's applicability and relevance further.

Concluding Remarks

In conclusion, MedIMeta represents a significant stride toward enhancing the interoperability and efficacy of ML models in medical imaging. By facilitating access to a broad array of medical imaging tasks and fostering the development of advanced CD-FSL algorithms, MedIMeta serves as a crucial resource for researchers aiming to tackle the nuanced challenges within the field of medical image analysis.