CXR segmentation/Object detection

Project Summary

The goal of this project is to develop a machine learning model that can accurately segment findings in chest X-ray (CXR) images using new algorithms. CXR is a common diagnostic tool used in the medical field to identify various respiratory and cardiac conditions. However, analyzing CXR images can be time-consuming and require specialized expertise.

By leveraging machine learning techniques, we aim to develop a model that can automatically segment CXR images, making the process more efficient and less dependent on human interpretation. The model will be trained on a large dataset of CXR images with corresponding annotations of different findings, such as lung nodules, pneumothorax, and pleural effusions.

Dataset

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Project outline

Use new segmentation algorithm (SAM by Facebook) to train the algorithm to segment findings on CXR.

We will use the above data set to train the algorithm and will validate the final model on data from multiple other publicly available datasets.

We will evaluate the model's accuracy using metrics such as precision, recall, and F1 score, and compare it to existing state-of-the-art approaches.

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