Summary: Create a tool to automatically label specified disease like pnumothorax, hemorrhage etc from xray images.
The tool should be developed as an standalone tool, and should have the options to draw labels of image segmentation and bounding box detection automatically from ML models. Automatic labeling would assist radiologist in their manual work.
Required Skills: Python, Deep Learning, Machine Learning, JS, ML Frameworks
Do proper research of state of the art models for detection and segmentation, available opensource medical dataset and how a model can be created or enhanced.
Please develop a POC and provide link to your github repo. The basic POC should have the facility to upload dataset/images, choose labelling type (bounding boxes for POC), and the pretrained model should be able to label it. You can provide options to select model as well (like Faster RCNN, Retinanet, Yolo, etc). It should be a standalone web app, preferably in Python frameworks (Django, Flask) but other frameworks are also considered.
Comprehensive research of existing opensource datasets, frameworks and different state of the art models. It should be google doc or pdf with link shared here.
Minimal web app to let user add/upload image and tool should be able to provide label for it according to the deep models selected by user.
Github repo should be shared here.
- Students are free to improvise the features and bring their own features with proper reasoning.
The requirement for main project will be updated over period of time, but POC should have the features mentioned above.