arXiv Computer Vision
Agent-Driven Generative Priors for Pathology Image Synthesis and Segmentation
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ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning
arXiv:2610.29090v1 Announce Type: new Abstract: Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, th...
arXiv Computer Vision
Oct 7
Scalable next-scale autoregression for medical image generation across anatomical regions
arXiv:2602.14512v3 Announce Type: replace Abstract: Autoregressive pretraining has been key to the scalability of large language models, yet medical generative foundation models remain predominantly...
arXiv Computer Vision
Oct 21
A Multi-Agent Framework for Automated Machine Learning with Evidence-based Planning for Medical Image Analysis
arXiv:2610.28410v1 Announce Type: new Abstract: Deep learning has become the default tool for medical image analysis, driven by the open-source movement. However, achieving the best performance on a...
arXiv Computer Vision
Aug 25
GET: Generative Embedding Translation for Medical Image Segmentation
arXiv:2608.22619v1 Announce Type: cross Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...
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Oct 21
ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning
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arXiv Machine Learning
Sep 4
A Real-Calibrated Synthetic-First Data Engine
The paper introduces the Real‑Calibrated Synthetic‑First Data Engine, a modular pipeline that integrates controllable diffusion‑based synthetic image generation with multi‑stage curation, filtering, and optional uncertainty‑driven selection and human verification. Designed as a CLI‑based framework, it allows independent configuration of generation, filtering, selection, and validation modules to enhance reproducibility and flexibility in real‑world data workflows. Empirical tests on human pose estimation demonstrate that synthetic data can boost a real‑data baseline when used as low‑cost augmentation, though synthetic‑only training still lags behind real‑only performance, underscoring the importance of data‑centric orchestration in low‑data regimes.