An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing

• URL: http://arxiv.org/abs/2604.21936v1
• Date: Tue, 31 Mar 2026 19:28:11 GMT
• Title: An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing
• Abstract summary: We present an artifact-based agent framework to augment medical image processing.<n>We evaluate the framework on real-world clinical CT and MRI cohorts.
• Score: 5.455908500614314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends beyond model design to require dataset-aware workflow configuration and provenance tracking. Two requirements therefore become central: \textbf{adaptability}, the ability to configure workflows according to dataset-specific conditions and evolving analytical goals; and \textbf{reproducibility}, the guarantee that all transformations and decisions are explicitly recorded and re-executable. Here, we present an artifact-based agent framework that introduces a semantic layer to augment medical image processing. The framework formalizes intermediate and final outputs through an artifact contract, enabling structured interrogation of workflow state and goal-conditioned assembly of configurations from a modular rule library. Execution is delegated to a workflow executor to preserve deterministic computational graph construction and provenance tracking, while the agent operates locally to comply with most privacy constraints. We evaluate the framework on real-world clinical CT and MRI cohorts, demonstrating adaptive configuration synthesis, deterministic reproducibility across repeated executions, and artifact-grounded semantic querying. These results show that adaptive workflow configuration can be achieved without compromising reproducibility in heterogeneous clinical environments.

Related papers

• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• DrawAI: Agentic Benchmark and Workflow for Making Raster Images Editable [10.070492917829995]
  We introduce DrawAI, comprising an agentic benchmark, DrawAI-Bench, and a reconstruction workflow, DrawAI-Flow.<n>We formulate image-to-editable reconstruction, which recovers a structured, directly manipulable artifact from an image while preserving its visual and semantic content.<n>The results show that reconstruction quality and costs vary substantially across model-harness configurations, while DrawAI-Flow consistently improves editable structure.
  arXiv  Detail & Related papers  (2026-08-01T09:05:06Z)
• Adaptive Multimodal Agents-Based Framework for Automatic Workflow Execution [1.2132786136735432]
  We propose a novel multi-agent framework that achieves automatic workflow execution through a distinct two-phase pipeline.<n>During inference, agents leverage Adaptive Retrieval-Augmented Generation (RAG) over this fixed, pre-established graph.<n>We validate our framework in a real-world context, demonstrating its ability to maintain high reliability and semantic awareness even with limited training data.
  arXiv  Detail & Related papers  (2026-05-27T15:23:22Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization [6.428928591765431]
  Operations research with large language models (LLMs) remains limited by hand-crafted reasoning.<n>We propose EvoOR-Agent, a co-evolutionary framework for automated optimization.<n>Case studies and ablation analyses indicate that explicit architecture evolution and graph-supported reasoning-trajectory search contribute to both performance improvement and structural interpretability.
  arXiv  Detail & Related papers  (2026-04-20T01:44:18Z)
• Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging [92.90858286761515]
  Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks.<n>We propose the first task-adapted CS-MRI from the information-theoretic perspective to simultaneously achieve probabilistic inference for uncertainty prediction.<n>We leverage amortized optimization and construct tractable variational bounds for mutual information to jointly optimize sampling, reconstruction, and task-inference models.
  arXiv  Detail & Related papers  (2026-04-14T13:23:19Z)
• Layout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers [57.54843029965778]
  Controllable pathology image synthesis requires reliable regulation of spatial layout, tissue morphology, and semantic detail.<n>We propose In-Context Diffusion Transformer (IC-DiT), a layout-aware generative model that incorporates spatial layouts, textual descriptions, and visual embeddings into a unified diffusion transformer.<n>IC-DiT achieves higher fidelity, stronger spatial controllability, and better diagnostic consistency than existing methods.
  arXiv  Detail & Related papers  (2026-03-11T06:14:11Z)
• Agentic Scientific Simulation: Execution-Grounded Model Construction and Reconstruction [0.0]
  This paper investigates agentic scientific simulation, where model construction is organized as an execution-grounded interpret-act-validate loop.<n>We present JutulGPT, a reference implementation built on the fully differentiable Julia-based reservoir simulator JutulDarcy.
  arXiv  Detail & Related papers  (2026-02-27T15:42:05Z)
• Monadic Context Engineering [59.95390010097654]
  This paper introduces Monadic Context Engineering (MCE) to provide a formal foundation for agent design.<n>We demonstrate how Monads enable robust composition, how Applicatives provide a principled structure for parallel execution, and crucially, how Monad Transformers allow for the systematic composition of these capabilities.<n>This layered approach enables developers to construct complex, resilient, and efficient AI agents from simple, independently verifiable components.
  arXiv  Detail & Related papers  (2025-12-27T01:52:06Z)
• Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection [59.04089915447622]
  ForenAgent is an interactive IFD framework that enables MLLMs to autonomously generate, execute, and refine Python-based low-level tools around the detection objective.<n>Inspired by human reasoning, we design a dynamic reasoning loop comprising global perception, local focusing, iterative probing, and holistic adjudication.<n>Experiments show that ForenAgent exhibits emergent tool-use competence and reflective reasoning on challenging IFD tasks.
  arXiv  Detail & Related papers  (2025-12-18T08:38:44Z)
• MedDCR: Learning to Design Agentic Workflows for Medical Coding [55.51674334874892]
  Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes.<n>We present MedDCR, a closed-loop framework that treats design as a learning problem.<n>On benchmark datasets, MedDCR outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-11-17T13:30:51Z)
• MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classification [0.0]
  MAGIC-Flow is a conditional multiscale normalizing flow architecture that performs generation and classification within a single framework.<n>We show how this ensures exact likelihood and stable optimization, while invertibility enables explicit visualization of sample likelihoods.<n>We evaluate MAGIC-Flow against top baselines using metrics for similarity, fidelity, and diversity.
  arXiv  Detail & Related papers  (2025-10-24T23:11:25Z)
• Scan-do Attitude: Towards Autonomous CT Protocol Management using a Large Language Model Agent [39.72587188702086]
  Large Language Model (LLM)-based agent framework is proposed to assist with the interpretation and execution of protocol configuration requests.<n>The agent combines in-context-learning, instruction-following, and structured toolcalling abilities to identify relevant protocol elements and apply accurate modifications.
  arXiv  Detail & Related papers  (2025-09-24T16:04:11Z)
• Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement [75.9289887536165]
  We present a hierarchical abstraction approach to uncover underlying entities. We show how to learn a correspondence between intervening on states of entities in the agent's model and acting on objects in the environment. We use this correspondence to develop a method for control that generalizes to different numbers and configurations of objects.
  arXiv  Detail & Related papers  (2023-03-20T18:19:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.