TissueLab: Adaptive AI for Medical Imaging

• TissueLab is a co-evolving AI ecosystem that integrates modular imaging tools with human-in-the-loop adaptations for clinical research.
• It orchestrates standardized image analysis modules via an LLM-driven workflow to deliver transparent, rapid, and reproducible results.
• Its open-source design supports sustainable innovation, enabling rapid adjustments and validated performance across diverse medical imaging tasks.

TissueLab is a co-evolving agentic AI ecosystem for medical imaging analysis, designed to unify, automate, and adapt advanced computational workflows for clinical and translational research applications. At its core, TissueLab employs a LLM as an orchestrator, coordinating modular image analysis tools—referred to as tool factories—across pathology, radiology, and spatial omics domains. The system emphasizes explainable real-time interactivity, expert-in-the-loop adaptation, transparent workflow generation, and sustainable open-source development (Li et al., 24 Sep 2025).

1. System Architecture and Core Components

TissueLab’s architecture is modular and layered, featuring:

• LLM Workflow Orchestrator: The LLM parses direct user queries (e.g., “Compute tumor-to-duct ratio in this region…”), automatically plans analytic workflows, and invokes modular analysis nodes (task plugins) through structured commands and semantic function calls.
• Tool Factories and Standardized Plugins: Each image analysis task (segmentation, feature extraction, multimodal fusion, etc.) is encapsulated as a “task node” with a standardized interface: each module can be initialized, provided with inputs, executed, and produce outputs that conform to clear schemas. Task nodes are assembled into a directed acyclic graph (DAG) representing the workflow plan, with dependencies managed via topological sorting to maximize parallel execution of independent branches. For node $v$ with no incoming edges, $\mathrm{execute}(v)$ proceeds, output is stored, $v$ is removed, and subsequent dependencies are updated.
• Editable Memory Layer: Intermediate outputs—NumPy arrays, segmentation masks, CSVs, annotated overlays—are stored in an HDF5-based persistent database. This layer facilitates partial workflow re-execution and supports clinician inspection, revision, or annotation of any result stage.
• Data Access Layer: Semantic function-calling retrieves relevant image or tabular data from local storage or PACS systems, leveraging structured naming conventions and metadata.
• Visualization and Interaction Layer: The system provides real-time visual overlays and interfaces for users to view, correct, or annotate intermediate results and to rapidly trigger additional workflow rounds.

A schematic in the source text (see Figure 1) depicts the orchestration by the LLM, tool-factory plugins, and then the DAG-based execution phase, all linked through the editable memory.

2. Core Functionalities and System Features

TissueLab is built for real-time, explainable clinical imaging analysis:

• Automated Workflow Generation: On receiving a clinical query (e.g., measuring colon tumor invasion depth), TissueLab plans and executes the necessary workflow—segmenting regions, extracting contours, and computing measurements—often within an hour for initial analysis.
• Human-in-the-Loop Adaptation: Intermediate outputs are visualized for clinician review; corrections and annotations are interactively fed back. The memory layer ensures no redundancy or repeated work, and this structure enables active-learning retraining of downstream models in minutes rather than hours or days.
• Tool Ecosystem Standardization: Modular plugin design allows seamless addition or swapping of new models (segmentation/classification networks, image preprocessing pipelines) across modalities, without altering the system’s core logic.
• Transparency: All intermediate and final outputs are accessible for auditing, annotation, and reuse—all steps in the pipeline are open and explainable.

These features yield rapid, reproducible, and guideline-aligned results, critical for research and clinical deployment.

3. Benchmark Performance and Quantitative Evaluation

TissueLab achieves state-of-the-art accuracy in diverse imaging tasks, outperforming both general-purpose vision–LLMs and previous agentic AI platforms:

Task	Metric	TissueLab (TLAgent)	Baseline Models
Colon Ca. invasion depth	Pearson corr. / MAE / RMSE	0.843 / 2.047mm / 3.091mm	GPT-5-vision MAE ≫ 1000 mm; low corr.
Lymph node metastasis	Weighted F1 / Accuracy	>0.926 / 91.9%	Baselines: F1 < 0.2; failed tasks
Prostate: Tumor-to-duct ratio	Accuracy after rapid feedback	99.8% (post 8 min)	Not attainable
Chest X-ray diagnosis	AUC improvement	+0.193 over VLMs	Lower
3D Radiology (Fatty liver, ICH)	AUC, Kappa	Expert-level	Lower, less reliable

In the reported benchmarks, alternative large multimodal models (e.g., GPT-5-vision, LLaVA variants) displayed poor or failed performance, often with large errors and inability to complete structured tasks under information bottlenecks.

4. Learning, Adaptation, and Co-Evolution

TissueLab employs continuous learning at multiple system layers:

• Active Learning: All expert corrections are stored as supervised data; these are used to fine-tune classification/segmentation nodes immediately after feedback (e.g., 82.1% to 94.9% tumor cell identification accuracy within 30 minutes of iterative input).
• Model Candidate Pool and Policy Adaptation: For each new clinical scenario, the orchestrator maintains a ranked candidate pool of models per task. Performance feedback updates rankings and decision policies, ensuring deployment adaptively follows evolving best practice.
• No Need for Massive Retraining: Unlike foundation models requiring expensive retraining for each new context, TissueLab adapts to unseen disease states within minutes using active-learning and lightweight module tuning.

These capabilities enable rapid deployment and effective adaptation in the clinic, critical for real-world translational use.

5. Translational Impact and Applications

TissueLab’s modular and adaptable design translates directly into enhanced performance in clinical research and diagnostic practice:

• Pathology: Accelerates and standardizes quantification tasks such as tumor invasion depth in colorectal cancer, tumor-to-duct ratio in prostate cancer, and glomerular counting in renal pathology.
• Radiology: Enables reproducible measurement of disease burden in 3D CT/MRI (fatty liver grading, intracranial hemorrhage), supporting treatment planning and prognosis.
• Spatial Omics Integration: Combines histological feature extraction with spatial transcriptomic clustering, improving accuracy in complex tissue characterization (e.g., kidney glomerulus quantification).
• General Research Acceleration: Open intermediate results and workflow transparency foster trust, enable rapid experimental modifications, and permit integration with evolving scientific and clinical guidelines.

6. Open-Source Ecosystem and Sustainability

TissueLab is distributed as a sustainable open-source ecosystem with multi-platform support (Windows, macOS, Linux) and a publicly accessible web portal (tissuelab.org):

• Community Collaboration: Facilitates contributions of novel models, datasets, and workflow templates by both researchers and clinicians.
• Sustainable Evolution: The modular tool-factory interface allows rapid uptake of new models/algorithms as methodologies advance, without monolithic retraining.
• Transparency and Trust: Open algorithms and accessible intermediate results ensure that every analytic decision is auditable, supporting regulatory compliance.
• Customization and Rapid Experimentation: Users can tailor and extend analysis pipelines to address emerging investigative or clinical questions, avoiding vendor lock-in or opaque “black box” decision-making.

Open source is therefore fundamental to the platform’s continuous improvement, broad adoption, and integration into translational workflows.

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TissueLab represents a comprehensive, adaptive infrastructure for explainable, high-throughput, and interactive medical imaging analysis, unifying modern agentic AI principles, modular tool orchestration, and collaborative open-source development for end-to-end translational impact (Li et al., 24 Sep 2025).