mlsanityData to 13 Oct 2026

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

arXiv 2404.16000 ↗First posted 24 Apr 2024cs.CV cs.LGcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 18 of its 54 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

18 of 54 references.

References · 18

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6 more
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 6.

12 papers.

  1. 2025On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?cited by 10
  2. 2024Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imagingcited by 8
  3. 2025Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shiftcited by 3
  4. 2024Privacy-preserving datasets by capturing feature distributions with Conditional VAEscited by 2
  5. 2024Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impactcited by 2
  6. 2025T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysiscited by 2
  7. 2026Universal Algorithm-Implicit Learningcited by 1
  8. 2026The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imagingcited by 0
  9. 2026Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detectioncited by 0
  10. 2026FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settingscited by 0
  11. 2026Assessing VLM Reliability for Medical Image Quality Evaluation Under Corruption and Biascited by 0
  12. 2026UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasoundcited by 0