13 Using segmentation to find suspected nodules
This chapter covers
- Segmenting data with a pixel-to-pixel model
- Performing segmentation with U-Net
- Understanding mask prediction using Dice loss
- Evaluating a segmentation model’s performance
In the last four chapters, we have accomplished a lot. We’ve learned about CT scans and lung tumors, datasets and data loaders, and metrics and monitoring. We have also applied many of the things we learned in part 1, and we have a working classifier. We are still operating in a somewhat artificial environment, however, since we require hand-annotated nodule candidate information to load into our classifier. We don’t have a good way to create that input automatically. Just feeding the entire CT into our ...
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