Hello everyone,

I am currently starting to do research on using contrastive learning for medical image segmentation. From all the papers I have read, I am trying to use SimCLR as pretraining for image segmentation, since I am dealing with medical images I will use the Unet for the downstream task.

My question is how do I use the SimCLR as pretraining and is there any good code baseline for this?

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draconicmoniker t1_ish3n4m wrote on October 15, 2022 at 11:02 PM

You may want to check out pip install self-supervised https://github.com/KeremTurgutlu/self_supervised which benchmarks several selfsupervised methods, here's a SimCLR tutorial https://keremturgutlu.github.io/self_supervised/01%20-%20training_simclr_iwang.html

Hulord OP t1_ispvozi wrote on October 17, 2022 at 8:30 PM

Thank you so much!

Status_Ad6022 t1_isj4dfd wrote on October 16, 2022 at 11:27 AM

I recently did some self supervised pretrainings (simclr, moco, Barlow twins) and used the vissl library for that which has worked pretty well for me (https://github.com/facebookresearch/vissl)

Hulord OP t1_ispvr2i wrote on October 17, 2022 at 8:31 PM

Thanks this was what i was looking for

Hulord OP t1_isth4xm wrote on October 18, 2022 at 4:10 PM

I´m having a bit of a struggle trying to pre-train using my data. I'm using a MedMNIST dataset (https://medmnist.com), specifically the BreastMNIST. I have the data in a DataLoader but can´t really understand how to load this dataloader to the configuration of the model by reading the documentation. Does it only work by having a downloaded dataset?

fastglow t1_isg4w7v wrote on October 15, 2022 at 6:51 PM

How about this: https://github.com/bayer-science-for-a-better-life/contrastive-reconstruction

DeepGamingAI t1_isigqfm wrote on October 16, 2022 at 6:07 AM

https://github.com/vturrisi/solo-learn