Efficient continual adaptation
Adapt a medical segmentation foundation model sequentially while measuring retention, forgetting, segmentation quality, and compute cost.
R&D stage
Medical Intelligence Adaptation
MedIA
Continual adaptation infrastructure for medical image segmentation
MedIA helps medical AI teams adapt segmentation models to new datasets, domains, and clinical tasks without retraining large foundation models from scratch.
Medical SAM3Continual learningAWS-ready experimentation
Adaptation run
Dataset-incremental protocol
Datasets
masks + prompts
Adapters
Low rank adapters
GPU jobs
AWS orchestration
Metrics
Dice / IoU / forgetting
Governance
IAM + budgets
Goal: learn new segmentation tasks while quantifying retention, forgetting, and compute cost.
Problem
Medical segmentation models need to keep learning after the first training run
The core challenge is a practical continual-learning problem in changing medical imaging environments.
Medical imaging environments are not static: new modalities, devices, hospital protocols, annotation styles, and clinical tasks appear over time.
Re-training large segmentation models from scratch is expensive, slow, and often unrealistic for teams with constrained compute budgets.
Historical medical data may be unavailable because of privacy, governance, storage, or institutional access constraints.
Sequential adaptation can cause catastrophic forgetting, where performance on previous tasks degrades after learning a new one.
Solution
A research-driven adaptation layer for foundation segmentation models
MedIA is designed as infrastructure for experimentation, benchmarking, and future medical AI workflows.
Parameter-efficient learning
Prioritize prompt adapters and low-rank updates instead of full-model retraining, keeping experiments feasible for early teams.
Prompt-driven interface
Use semantic prompts such as polyp, retinal vessel, breast lesion, or gland as a flexible control layer for medical segmentation tasks.
Product vision
A modular platform for model adaptation experiments
The product vision is to provide a research-driven adaptation layer for foundation segmentation models, with a future API for model adaptation and inference.
01
Dataset and mask preparation pipeline
02
Prompt-based task definition
03
Model adaptation engine
04
Continual learning experiment runner
05
Evaluation and benchmarking dashboard
06
Model versioning and artifact tracking
07
Cloud-based GPU training workflows
08
Future API for model adaptation and inference
Technology
Built around Medical Segment Anything Foundation Model adaptation
The technical core is based on a research study on efficient continual learning over medical segmentation foundation models.
Medical SAM3 as the prompt-driven medical segmentation foundation model
Prompt adapters for lightweight adaptation in the prompt and mask-decoder pathway
LoRA for low-rank parameter-efficient updates over selected transformer components
Variations of LoRA adapter as a preservation-oriented variants to study interference and retention
Incremental adapter merging to avoid unbounded model growth or historical data storage
Continual learning protocol with sequential dataset-incremental or domain-incremental tasks
Segmentation metrics including Dice, IoU, and HD95
Continual learning metrics including Average Accuracy, Forgetting, and Backward Transfer
Efficiency metrics including trainable parameters, peak GPU memory, accumulated parameters, and training time
Early use cases
Useful for teams that need credible adaptation evidence before deployment claims
Research labs
Compare adaptation strategies across public or internal datasets while keeping experiments reproducible.
Medical AI startups
Evaluate whether a segmentation foundation model can be adapted to a new imaging domain before committing to expensive retraining.
Hospital innovation teams
Prototype model adaptation workflows under governance constraints without claiming production clinical deployment.
IVF and microscopy research
Explore embryo, blastomere, fragment, gland, or cell segmentation workflows in a research validation setting.
Imaging software teams
Benchmark prompt-driven segmentation adaptation for radiology, pathology, endoscopy, ophthalmology, and ultrasound prototypes.
Research validation
Public, reproducible segmentation benchmarks first
Evaluation is framed as research validation on public datasets, not clinical validation or regulatory clearance.
Endoscopy polyp segmentation with Kvasir-SEG-style tasks
Retinal vessel segmentation with fundus imaging datasets
Breast ultrasound lesion segmentation with BUSI-style data
Histopathology gland segmentation with GlaS-style tasks
Potential extension to embryo segmentation with CleavageEmbryo-style data
AWS infrastructure
AWS infrastructure plan for reproducible continual-learning experiments
AWS gives MedIA a credible path from laptop-scale research to repeatable GPU training, tracked artifacts, controlled access, and cost-aware experiment orchestration.
Experiment flow
1Curated datasets and masks
2Containerized training jobs
3Sequential adaptation runs
4Metrics and artifacts
5Dashboard and future API
Amazon S3
Store datasets, masks, canonical prompts, checkpoints, trained adapters, evaluation reports, and model artifacts.
Amazon EC2 GPU / SageMaker
Run Medical SAM3 adaptation, LoRA and OPLoRA training, benchmark evaluation, and reproducible GPU experiments.
Amazon ECR
Version Docker images for training, evaluation, and future inference workers.
AWS Batch / Step Functions
Orchestrate sequential experiments across tasks, orders, methods, and evaluation checkpoints.
Amazon CloudWatch
Collect logs, job metrics, resource usage, and operational alerts for long-running experiments.
IAM
Control access to private datasets, experiment artifacts, compute resources, and deployment roles.
AWS Budgets
Track GPU spend, set alerts, and keep R&D infrastructure cost-aware from the beginning.
DynamoDB / RDS
Store experiment metadata, model registry records, dataset versions, and adaptation run lineage.
Roadmap
Path from research prototype to early platform
The project is early-stage. Each phase turns the research workflow into a infrastructure.
Phase 1
Research prototype and reproducible training pipeline
Phase 2
Continual learning benchmark
Phase 3
Cloud experiment orchestration on AWS
Phase 4
Early product dashboard
Phase 5
Partner validation
Business model
Commercial direction without premature traction claims.
- B2B SaaS for medical AI teams that need reproducible model adaptation experiments
- Cloud-based experimentation and continual-learning benchmark platform
- Enterprise or research lab subscriptions for controlled workflows and artifact tracking
- Custom model adaptation workflows for specialized medical imaging domains
- Future API-based usage for adaptation jobs, benchmark reports, and inference workflows
Founder
Santiago Tomas Torres
Final-year Artificial Intelligence Engineering student
Focused on machine learning, computer vision, medical AI, LLM systems, and applied AI infrastructure. MedIA is connected to research on efficient continual learning for medical segmentation foundation models.
Contact
Discuss research collaboration or early technical partnership.
Open to research collaborations and early technical partnerships.