Towards Autonomous and Auditable Medical Imaging Model Development

arXiv:2607.10522 · cs.CV, cs.AI · Submitted 2026-07-12 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-07-12

Updated: 2026-09-27

Code: https://github.com/CUHK-AIM-Group/AMID

Terminology

• Verification — 29× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Foundation Model — 13× in this paper · explained in 3 episodes
• Two-Stage Optimization — 10× in this paper · explained in 2 episodes
• Data-Conditioned Method Planning — 9× in this paper
• Accuracy — 8× in this paper · explained in 1 episode
• Foundation Models — 7× in this paper · explained in 7 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• nnU-Net — 6× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Provenance — 5× in this paper · explained in 4 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Multi-Agent Framework — 4× in this paper · explained in 3 episodes
• AI Agents — 3× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Multi-agent Collaboration — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Agentic AI — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Agentic AI systems — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Anisotropy — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Standardized Metrics — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• MONAI: An open-source framework for deep learning in healthcare
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
• MARS: Modular Agent with Reflective Search for Automated AI Research
• Barbarians at the Gate: How AI is Upending Systems Research
• AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents
• MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery
• Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
• Phikon-v2, A large and public feature extractor for biomarker prediction
• AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
• Camyla: Scaling Autonomous Research in Medical Image Segmentation
• Accelerating scientific discovery with Co-Scientist
• Segment Anything for Histopathology
• DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration
• REPRO-Bench: Can Agentic AI Systems Assess the Reproducibility of Social Science Research?
• MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
• Can Coding Agents Reproduce Findings in Computational Materials Science?
• AIDE: AI-Driven Exploration in the Space of Code
• Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
• Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge

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