arXiv:2606.25770v1 Announce Type: new Abstract: Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities. We introduce a statistically grounded patient augmentation approach that generates realistic patients directly in embedding space. Using Gaussian Mixture Models as a probabilistic clustering approach on pooled instance embeddings from all patients, our method learns disease-specific "recipes"-statistical distributions of instances across unsupervised clusters. New patients are then generated by sampling embeddings
The rapid advancements in AI, particularly in generative models and embedding techniques, make statistically grounded patient augmentation in embedding spaces a logical next step to address data scarcity in medical AI.
This research offers a novel approach to overcome a critical bottleneck in medical AI development by realistically augmenting patient data, potentially accelerating drug discovery, diagnostic tool development, and personalized medicine, especially for rare and complex diseases.
The ability to generate synthetic medical data directly in embedding space using probabilistic clustering changes how data-scarce medical AI models can be trained and validated, moving beyond traditional data augmentation techniques.
- · Medical AI developers
- · Rare disease research
- · Pharmaceutical companies
- · Healthcare providers in underserved areas
- · Traditional data collection methods for rare diseases
Improved performance and broader applicability of AI models in medical diagnostics and treatment planning.
Reduced cost and time for developing AI solutions in healthcare, potentially leading to more accessible medical technologies.
Ethical and regulatory discussions around the use of synthetically generated patient data in clinical settings could intensify.
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Read at arXiv cs.LG