yashsharma / c2c Goto Github PK
View Code? Open in Web Editor NEWImplementation of Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification approach.
License: MIT License
Implementation of Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification approach.
License: MIT License
Hi YashSarma, thanks for your working and opensource the project!
I'm very interested in this but i'm a new learner, i have some questinos about the patches.
I wonder how you get the patches from a wsi and organize the train csv?
Since only WSI-level labels were available.
I would like to know if it is possible to have access to the data used for training.
Thanks
Hello, thank you for nice work and open-sourcing your project.
Recently, I attempted to train the C2C model using my own dataset and monitored its accuracy through tensorboard. The model appeared to train properly; however, I encountered an issue with the validation accuracy. During the validation phase, the model always outputs 'positive' regardless of the input data.
Upon further investigation, I noticed a significant disparity in the output distribution of the ResNet backbone during the validation phase compared to the training phase. By enabling the train mode using model.train() in the evaluation code, the model's outputs seem to be correct once again.
I have a few questions regarding this phenomenon:
Although I have not yet tried the CAMELYON16 dataset, I would like to gain a clear understanding of the exact environment before conducting any experiments.
Thank you for your time and consideration.
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