Closed brisker closed 6 years ago
@zizhaozhang I am very interested in this paper. Hope you can provide some answers for my questions, thanks a lot!
so one of the idea this paper wants to tell us is that , using both image and text to train a model, and the image classification accuracy will increase? What reason can explain this accuracy increase?
it seems that your report can be summarized as 5 2-class or 3-class classification problems. So in the real medical report, I wonder that, does the doctor write sentences like "moderate crowding of the nuclei can be seen" , or write a crowding label(for example, 1 indicates mild crowding, 2 indicates moderate crowding, 3 indicates severe crowding)? If write labels, why not regard this a pure image classification problem?( I mean 5 classification problems , regarding the 5 cell appearances)
Besides, I observed that the descriptions are mainly about the cell appearance features, so why not directly using cell segmentation results to get the cell appearance features, since this seems more reasonable and reliable than learning from the report? (Because the cell segmentation results can not be integrated into a deep learning framework?)
Thanks !
@zizhaozhang I have already emailed you(zizhao@cise.ufl.edu) for further questions. Waiting for your answer. Thanks!