Advanced AI for Pancreatic Imaging Analysis
Budget: $250 – $750 USD
Project Title: Automated Pancreatic Segmentation System
Project Description:
We are seeking an experienced developer to create an advanced AI-based pancreatic segmentation system for medical imaging. This project involves developing PanSegNet, a robust segmentation model designed to analyze CT and MRI scans with high accuracy. PanSegNet will combine the strengths of nnUNet and Transformer networks, incorporating a novel linear attention module for efficient volumetric computation.
The system will support both cross-modality (CT, T1-weighted MRI, and T2-weighted MRI) and cross-center accuracy, providing a critical tool for the diagnosis and follow-up of pancreatic diseases. The final product should be user-friendly, well-documented, and ready for deployment in clinical workflows.
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Key Features and Requirements:
1. Model Development:
Implement PanSegNet combining nnUNet and Transformer architecture.
Integrate a novel linear attention module for volumetric segmentation.
2. Dataset Management:
Handle a dataset of 767 MRI scans (T1 and T2) from 499 participants.
Include 1,350 publicly available CT scans for benchmarking purposes.
3. Performance Metrics:
Achieve Dice coefficients of:
CT: 88.3%
T1 MRI: 85.0%
T2 MRI: 86.3%
Evaluate segmentation accuracy using Dice, Hausdorff distance (HD95), and Cohen’s kappa for inter- and intra-rater agreement.
4. Technology Stack:
Deep Learning Frameworks: PyTorch/TensorFlow.
Medical Imaging Tools: SimpleITK, NiBabel, ITK-SNAP.
Data Preprocessing and Analysis: Python, NumPy, Pandas.
Statistical Evaluation: SciPy, Scikit-learn.
5. Deliverables:
Fully trained PanSegNet model for CT and MRI segmentation.
Scripts for preprocessing, segmentation, and evaluation.
Documentation for usage and integration into clinical workflows.
Support for deployment and initial maintenance.
Ideal Candidate:
Expertise in deep learning and medical imaging analysis.
Experience with nnUNet, Transformer models, and attention mechanisms.
Strong Python programming skills and familiarity with medical imaging libraries.
Ability to conduct detailed statistical analyses of model performance.
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Budget and Timeline:
Budget: Negotiable based on experience and milestones.
Timeline: To be discussed and agreed upon based on the project scope.
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If you’re passionate about AI and medical imaging and have the skills to bring this project to life, we’d love to hear from you. Let’s work together to advance the field of pancreatic disease diagnosis and care!
Project Description:
We are seeking an experienced developer to create an advanced AI-based pancreatic segmentation system for medical imaging. This project involves developing PanSegNet, a robust segmentation model designed to analyze CT and MRI scans with high accuracy. PanSegNet will combine the strengths of nnUNet and Transformer networks, incorporating a novel linear attention module for efficient volumetric computation.
The system will support both cross-modality (CT, T1-weighted MRI, and T2-weighted MRI) and cross-center accuracy, providing a critical tool for the diagnosis and follow-up of pancreatic diseases. The final product should be user-friendly, well-documented, and ready for deployment in clinical workflows.
---
Key Features and Requirements:
1. Model Development:
Implement PanSegNet combining nnUNet and Transformer architecture.
Integrate a novel linear attention module for volumetric segmentation.
2. Dataset Management:
Handle a dataset of 767 MRI scans (T1 and T2) from 499 participants.
Include 1,350 publicly available CT scans for benchmarking purposes.
3. Performance Metrics:
Achieve Dice coefficients of:
CT: 88.3%
T1 MRI: 85.0%
T2 MRI: 86.3%
Evaluate segmentation accuracy using Dice, Hausdorff distance (HD95), and Cohen’s kappa for inter- and intra-rater agreement.
4. Technology Stack:
Deep Learning Frameworks: PyTorch/TensorFlow.
Medical Imaging Tools: SimpleITK, NiBabel, ITK-SNAP.
Data Preprocessing and Analysis: Python, NumPy, Pandas.
Statistical Evaluation: SciPy, Scikit-learn.
5. Deliverables:
Fully trained PanSegNet model for CT and MRI segmentation.
Scripts for preprocessing, segmentation, and evaluation.
Documentation for usage and integration into clinical workflows.
Support for deployment and initial maintenance.
Ideal Candidate:
Expertise in deep learning and medical imaging analysis.
Experience with nnUNet, Transformer models, and attention mechanisms.
Strong Python programming skills and familiarity with medical imaging libraries.
Ability to conduct detailed statistical analyses of model performance.
---
Budget and Timeline:
Budget: Negotiable based on experience and milestones.
Timeline: To be discussed and agreed upon based on the project scope.
---
If you’re passionate about AI and medical imaging and have the skills to bring this project to life, we’d love to hear from you. Let’s work together to advance the field of pancreatic disease diagnosis and care!