GSoC Week 9

 This week I worked on one big task which is,

- Integrating mammography cancer classification model

The Challenge

Initially I planned and still plan on integrating the RSNA mammography cancer classification top ranked model from kaggle. As I dug deep down into the code base of the top solution I realized it is going to take time to integrate it. It uses a YOLO model to detect the mammogram area to crop out and then uses a convnext model to perform classification. I already used a day to figure out integration aspects and how I can productionize it in the LH radiology application. Thinking about this complicated model and also integration strategies it was quiet overwhelming. So I wanted to somehow break down my tasks, strategize and integrate it.

Plan of Action

Meanwhile I remembered of the VGG16 model that I created to do mammography cancer classification at the start of GSoC. I decided to first integrate the VGG16 model since that would allow me to setup the codebase in the application like all the required endpoints, resources, schemas, etc. Once I am done with it I can then concentrate on integrating the RSNA kaggle model. I felt this would lighten the complexity and also I could show progress.

Results

I proceeded with my plan and started integrating the mammography model to the application. As I expected I came across with a lot of hiccups in model integration. There were a lot of moving parts since this was just the second model being integrated apart from the already integrated cheXnet model. I had to in places restructure the code base to easily handle integration.

By the end of the week I was able to integrate the model successfully with test cases. I would need to next week run through with my mentor on my integration for guidance on any corrections that may be required.

Some sample outputs from the Mammography cancer classification model,


I need to also work on removing the additional metadata like area, mean, std dev in the output image. Since this is just a classifier the outputs will always be pinned to the top left of the image.








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