Finetuning Cellpose-SAM

[licmn]

I have 17 spatial transcriptomics samples (1 sample shown) with paired H&E images. I am looking to predict cell types from H&E, and for that, I finetuned Cellpose-SAM model for predicting 13 classes.

I followed Cellpose semantic segmentation code (https://github.com/MouseLand/cellpose/blob/e3879a1cc58d4aa313d50977bb9b31ab11f89a2e/paper/cpsam/semantic.py), only changing there the number of labels. I used their hyperparameters as well:

# Hyperparameters:
rdrop=0.4 # In vit_sam.Transformer initialization
learning_rate = 5e-5 
weight_decay = 0.1 
batch_size = 8 
n_epochs = 500
bsize = 256
rescale = False 
scale_range = 0.5

Also, I’ve read about the mean cell diameter parameter for cellpose, but have not used it. Here is the loss curve from finetuning. I can see that past epoch 150-200 the model starts to overfit on the training data without improving performance on the withheld test samples. I would truly appreciate any advice or feedback on how to fine-tune the model better. What would you try changing first in the finetuning process to improve training?

Here is the training curve

Thanks a lot for your help! Luisa

[Jiadalee]

@licmn pls lower down your n_epoch to 50-30 and batch_size to 2-3. You might also remove the rdrop and scale_range params.