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ivadomed / MEEG-Brainstorm

Repository for training MEEG datasets with the ivadomed framework in Brainstorm
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Predictions concentrate at the center of the time domain #12

Closed mpompolas closed 2 years ago

mpompolas commented 2 years ago

Just noticed the driving force of the low dice scores in the trained models.

All predictions are concentrated at the center of the time domain.


Here is an example of an overlap of all ground truths of the test set:

image

They are pretty much spread out in time (up-down axis on the first two views/columns)


However, if you take a look at the overlap of all predictions, they are all concentrated around the middle of the time dimension:

image


This appears to be consistent along all training configurations I tried.

@naga-karthik I just looked at your slide from yesterday and this behavior seems to hold there as well.

@andreanne-lemay @charleygros @mariehbourget any ideas on how to play with the config file to overcome this?

Here is an example config file used:

Config file ``` { "command": "train", "gpu_ids": [ 0 ], "path_output": "/home/GRAMES.POLYMTL.CA/u111358/data_nvme_u111358/EEG-ivado/epilepsy/distant_spikes_2_seconds/output_Epilepsy_EEG_runs_3_kernel_224_112", "model_name": "EEG_model", "debugging": true, "log_file": "log", "object_detection_params": { "object_detection_path": null, "safety_factor": [ 1, 1, 1 ], "gpu_ids": 0, "path_output": "/home/GRAMES.POLYMTL.CA/u111358/data_nvme_u111358/EEG-ivado/epilepsy/distant_spikes_2_seconds/output_Epilepsy_EEG_runs_3_kernel_224_112" }, "loader_parameters": { "path_data": [ "/home/GRAMES.POLYMTL.CA/u111358/data_nvme_u111358/EEG-ivado/epilepsy/distant_spikes_2_seconds/data_Epilepsy_EEG_runs_3" ], "subject_selection": { "n": [], "metadata": [], "value": [] }, "target_suffix": [ "_saw_EST_distant" ], "extensions": [], "roi_params": { "suffix": null, "slice_filter_roi": null }, "contrast_params": { "training_validation": [ "EEGspacedout2" ], "testing": [ "EEGspacedout2" ], "balance": {} }, "slice_filter_params": { "filter_empty_mask": false, "filter_empty_input": true }, "slice_axis": "sagittal", "multichannel": false, "soft_gt": false, "is_input_dropout": false }, "split_dataset": { "fname_split": null, "random_seed": 6, "split_method": "participant_id", "data_testing": { "data_type": null, "data_value": [] }, "balance": null, "train_fraction": 0.6, "test_fraction": 0.2 }, "training_parameters": { "batch_size": 128, "loss": { "name": "DiceLoss" }, "training_time": { "num_epochs": 100, "early_stopping_patience": 50, "early_stopping_epsilon": 0.001 }, "scheduler": { "initial_lr": 0.001, "lr_scheduler": { "name": "CosineAnnealingLR", "base_lr": 1e-05, "max_lr": 0.01 } }, "balance_samples": { "applied": false, "type": "gt" }, "mixup_alpha": null, "transfer_learning": { "retrain_model": null, "retrain_fraction": 1, "reset": true } }, "default_model": { "name": "Unet", "dropout_rate": 0.3, "bn_momentum": 0.9, "depth": 3, "is_2d": true }, "uncertainty": { "epistemic": false, 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naga-karthik commented 2 years ago

Thank you for the confirmation @mpompolas! Yes, I had observed something similar about the test predictions and I was wondering whether it had something to do with the location of the spikes in the dataset? I think it would be good to also visualize the data-augmented versions of the input so that we at least know what the model is getting as its inputs.

mpompolas commented 2 years ago

Related to 1023 and 26.

The slice_axis is transforming the NIFTI files during the 3D training. slice_axis needs to be set to axial (the default was set to sagittal) for the way that the pipeline is designed for the inputs from Brainstorm.

All future MEEG config files will have: slice_axis='axial'.

Immediate improvement in training. The epileptic spike predictions are no longer concentrated in the middle.