Generate radiology reports from images
Foundation model to generate radiology report from radiology images
Overview
Radiology report writing in hospitals is a time consuming task that requires specialists such as radiologists to interpret images. The detailed information generated from these images are necessary for proper diagnosis. For accurate diagnosis, it requires experienced radiologists to interpret images and provide findings. On an average, it takes about 10 mins for radiologists to interpret images and this can be very time consuming in a hospital where radiologist has to view many images every single day. Automatic report generation can help reduce the burden.
How it works?
The model consists of an encoder that outputs features from an image and a decoder that generates words. For encoder, we use densenet model and for decoder, we used RoBerta model. The model was trained on large amount of dataset containing medical images with their radiology report.
Future work?
Currently, the radiology report generated provides impressions. In future, we plan to generate detailed radiology report. In addition, future plans involve building FM for both radiology and pathology.
Architecture
The model was trained on Amazon SageMaker with DenseNet being the encoder and RoBerta as a decoder model. The trained model was then deployed as a real-time endpoint invoked by Streamlit application running on EC2.
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