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Found 4 papers, 2 papers with code
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT
Deep learning for histopathology has been successfully used for disease classification, image segmentation and more.
Caption Generation
Histopathological Image Classification
+4
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers
In this work, we show that using an existing pre-trained Vision Transformer in a two-step process of first using it to encode 4096x4096 sized patches of the Whole Slide Image (WSI) and then using it as the encoder and an LSTM decoder for report generation, we can build a fairly performant and portable report generation mechanism that takes into account the whole of the high resolution image, instead of just the patches.
Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid
Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70. 48\%.
Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease
Physicians use biopsies to distinguish between different but histologically similar enteropathies.
