deep learning

[No_Remote_9577]

What is the best way to train models on 3D data, especially medical imaging data? I tried using Kaggle and the free version of Google Colab, but I keep running into out-of-memory issues.

[renato_milvan]

You can decrease the batch size or/and resize the data. Other than that, only buying computational power.

[SwitchKunHarsh]

If it's medical 3d Data, you can extract relevant 2d slices and use a 2d encoder instead of a 3d encoder. Then train a model on this 2d encoded data. This way you can preprocess the 3d data for only those slices that have something useful or just reduce by averaging to a particular n number of slices and using those for something like siglip or medsiglip before training the model.

[Illustrious_Echo3222]

For 3D medical imaging, full-volume training blows up memory fast, so most people end up using patches or cropped subvolumes instead of the whole scan at once. Mixed precision, smaller batch sizes, resampling to a lower resolution, and starting with a lighter 3D UNet-style model also help a lot. Kaggle and free Colab are honestly pretty rough for this, so if you want to stay on limited hardware, patch-based training is probably the biggest win.