Open shubhankar-git opened 1 week ago
Hey @shubhankar-git , thanks for opening this issue and reaching out! HoVer-NeXt was not trained on cytology data so its not validated for this kind of data. However, you could do a number of things:
If I misunderstood what you are trying to do, please let me know as well!
Hey @eliasbaumann Thanks for the response and the suggestions!
To clarify, I did not train the model on the Cytodark dataset; I used the pretrained lizard_convnextv2_large
model for inference. While I am not planning to train the model myself, I will definitely try the hyperparameter tuning and post-processing adjustments you mentioned.
I'll look into the changes in the constants file and also experiment with the hyperparameter search script for optimization. I’ll keep you posted on how it goes and get back to you if I run into any issues.
Thanks again for your help!
You could start by setting MAX_THRESHS_LIZARD to very large numbers, e.g. 100000. But even then, the model will always try to find nuclei and not cells. And in the dataset you are using, the entire cell is annotated.
Original image
Predicted
Ground Truth
I ran inference on the Cytodark dataset using the default parameters, but I noticed that a significant number of cells were mismatched between the predicted and ground truth results. I have attached the original Nissl image, the predicted output, and the ground truth image for reference.
Could you recommend hyperparameters or adjustments that can be applied to improve the cell segmentation results on this dataset? Any advice on tuning the model for better accuracy would be greatly appreciated.