Required stack
필요 기술
PythonMachine Learning (ML)Deep LearningModel DeploymentAmazon Web Services (AWS)AWS SageMakerAWS LambdaAWS API GatewayAWS S3AWS Route 53AWS ACMIAMHugging FaceDICOM3D Volumetric ReconstructionAPI DevelopmentJSON
Project brief
프로젝트 내용
deploy a dual-model pipeline on AWS.
Scope of Work
Dual-Model Deployment:
Deploy the Pillar-0 (Atlas architecture) for multi-finding classification across Chest, Abdomen, and Brain.
Integrate Sybil-1.5 for specialized future lung cancer risk prediction (1–6 year horizon).
Inference Pipeline & Report Generation:
deploy pipeline that takes zipped DICOM files, performs 3D volumetric reconstruction, and runs concurrent inference. inference.py script (attached). This script shows how to read the (Zip file), stack of the medical pictures (DICOMs) into a 3D block, and run both the Pillar-0 model (for findings) and Sybil-1.5 (for cancer risk)
requirements.txt -This tells AWS which specialized tools to install to read 3D medical images
AWS Cloud Architecture:
Host models on AWS SageMaker (GPU instances like ml.g5.2xlarge).
Implement an API Gateway + Lambda front-door.
Support the "Locker Pattern" for direct S3 uploads of large volumes (>10MB) via pre-signed URLs. Model Resources
Pillar-0 Collection: huggingface.co/collections/YalaLab/pillar-0
Sybil-1.5 Model: huggingface.co/YalaLab/Pillar0-Sybil-1.5 Deliverables
A live, secured AWS API endpoint.
A sample "Radiology Style" JSON and PDF output generated from a test CT.
When you upload to AWS, your bucket MUST look like this for the code:
s3://your-s3-bucket/
└── models/
└── dxpert-v1/
└── model.tar.gz
Scope of Work
Dual-Model Deployment:
Deploy the Pillar-0 (Atlas architecture) for multi-finding classification across Chest, Abdomen, and Brain.
Integrate Sybil-1.5 for specialized future lung cancer risk prediction (1–6 year horizon).
Inference Pipeline & Report Generation:
deploy pipeline that takes zipped DICOM files, performs 3D volumetric reconstruction, and runs concurrent inference. inference.py script (attached). This script shows how to read the (Zip file), stack of the medical pictures (DICOMs) into a 3D block, and run both the Pillar-0 model (for findings) and Sybil-1.5 (for cancer risk)
requirements.txt -This tells AWS which specialized tools to install to read 3D medical images
AWS Cloud Architecture:
Host models on AWS SageMaker (GPU instances like ml.g5.2xlarge).
Implement an API Gateway + Lambda front-door.
Support the "Locker Pattern" for direct S3 uploads of large volumes (>10MB) via pre-signed URLs. Model Resources
Pillar-0 Collection: huggingface.co/collections/YalaLab/pillar-0
Sybil-1.5 Model: huggingface.co/YalaLab/Pillar0-Sybil-1.5 Deliverables
A live, secured AWS API endpoint.
A sample "Radiology Style" JSON and PDF output generated from a test CT.
When you upload to AWS, your bucket MUST look like this for the code:
s3://your-s3-bucket/
└── models/
└── dxpert-v1/
└── model.tar.gz