# A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset

arXiv: https://arxiv.org/abs/2404.16000 · HTML: https://mlsanity.com/paper/2404.16000/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark](https://mlsanity.com/paper/1910.04867/) (2019)
- [TADAM: Task dependent adaptive metric for improved few-shot learning](https://mlsanity.com/paper/1805.10123/) (2018)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://mlsanity.com/paper/1903.03096/) (2019)
- [Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification](https://mlsanity.com/paper/2302.08909/) (2023)
- [Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark](https://mlsanity.com/paper/2104.02638/) (2021)
- [Sequential mastery of multiple visual tasks: Networks naturally learn to learn and forget to forget](https://mlsanity.com/paper/1905.10837/) (2019)

## Built on by (most cited first, 12 of 12)

- [On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?](https://mlsanity.com/paper/2505.15425/) (2025)
- [Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging](https://mlsanity.com/paper/2408.08058/) (2024)
- [Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift](https://mlsanity.com/paper/2509.09397/) (2025)
- [Privacy-preserving datasets by capturing feature distributions with Conditional VAEs](https://mlsanity.com/paper/2408.00639/) (2024)
- [Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impact](https://mlsanity.com/paper/2412.19124/) (2024)
- [T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysis](https://mlsanity.com/paper/2510.27265/) (2025)
- [Universal Algorithm-Implicit Learning](https://mlsanity.com/paper/2602.14761/) (2026)
- [The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging](https://mlsanity.com/paper/2602.21372/) (2026)
- [Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection](https://mlsanity.com/paper/2605.01563/) (2026)
- [FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settings](https://mlsanity.com/paper/2605.05553/) (2026)
- [Assessing VLM Reliability for Medical Image Quality Evaluation Under Corruption and Bias](https://mlsanity.com/paper/2607.01973/) (2026)
- [UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound](https://mlsanity.com/paper/2609.28610/) (2026)

## Abstract (arXiv)

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.