Open valosekj opened 7 months ago
It seems that the model is learning nothing for both fold_0
and fold_1
:
Potential future idea for another MULTI-CHANNEL model:
"channel_names": {
"0": "acq-ax_T2w",
"1": "compression_label"
},
But we currently do not have the compression labels for dcm-zurich-lesions-20231115
.
Since the multi-channel model with the default nnUNetTrainer
trainer was learning nothing (see my comment above), I tried the nnUNetTrainerDiceCELoss_noSmooth
trainer (i.e., without the smoothing term of the Dice loss) as done for the region-based model (see here).
The training with the nnUNetTrainerDiceCELoss_noSmooth
trainer finished successfully 🎉
Training the multi-channel nnUNetTrainerDiceCELoss_noSmooth
model for seed710
now (model name: Dataset603_DCMlesionsMultiChannelSeed710
).
nnUNetTrainerDiceCELoss_noSmooth__nnUNetPlans__3d_fullres/fold_1
This issue summarizes experiments related to
Dataset602_DCMlesionsMultiChannel
(multi-channel model trained using T2w_ax and SC seg as input channels to segment lesions).dataset.json
:nnUNetPlans.json
```json { "dataset_name": "Dataset602_DCMlesionsMultiChannel", "plans_name": "nnUNetPlans", "original_median_spacing_after_transp": [ 3.5999999046325684, 0.5, 0.5 ], "original_median_shape_after_transp": [ 15, 320, 319 ], "image_reader_writer": "SimpleITKIO", "transpose_forward": [ 0, 1, 2 ], "transpose_backward": [ 0, 1, 2 ], "configurations": { "2d": { "data_identifier": "nnUNetPlans_2d", "preprocessor_name": "DefaultPreprocessor", "batch_size": 31, "patch_size": [ 320, 320 ], "median_image_size_in_voxels": [ 320.0, 319.0 ], "spacing": [ 0.5, 0.5 ], "normalization_schemes": [ "ZScoreNormalization", "ZScoreNormalization" ], "use_mask_for_norm": [ false, false ], "UNet_class_name": "PlainConvUNet", "UNet_base_num_features": 32, "n_conv_per_stage_encoder": [ 2, 2, 2, 2, 2, 2, 2 ], "n_conv_per_stage_decoder": [ 2, 2, 2, 2, 2, 2 ], "num_pool_per_axis": [ 6, 6 ], "pool_op_kernel_sizes": [ [ 1, 1 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ] ], "conv_kernel_sizes": [ [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ] ], "unet_max_num_features": 512, "resampling_fn_data": "resample_data_or_seg_to_shape", "resampling_fn_seg": "resample_data_or_seg_to_shape", "resampling_fn_data_kwargs": { "is_seg": false, "order": 3, "order_z": 0, "force_separate_z": null }, "resampling_fn_seg_kwargs": { "is_seg": true, "order": 1, "order_z": 0, "force_separate_z": null }, "resampling_fn_probabilities": "resample_data_or_seg_to_shape", "resampling_fn_probabilities_kwargs": { "is_seg": false, "order": 1, "order_z": 0, "force_separate_z": null }, "batch_dice": true }, "3d_fullres": { "data_identifier": "nnUNetPlans_3d_fullres", "preprocessor_name": "DefaultPreprocessor", "batch_size": 2, "patch_size": [ 16, 320, 320 ], "median_image_size_in_voxels": [ 15.0, 320.0, 319.0 ], "spacing": [ 3.5999979972839355, 0.5, 0.5 ], "normalization_schemes": [ "ZScoreNormalization", "ZScoreNormalization" ], "use_mask_for_norm": [ false, false ], "UNet_class_name": "PlainConvUNet", "UNet_base_num_features": 32, "n_conv_per_stage_encoder": [ 2, 2, 2, 2, 2, 2, 2 ], "n_conv_per_stage_decoder": [ 2, 2, 2, 2, 2, 2 ], "num_pool_per_axis": [ 2, 6, 6 ], "pool_op_kernel_sizes": [ [ 1, 1, 1 ], [ 1, 2, 2 ], [ 1, 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ], [ 1, 2, 2 ], [ 1, 2, 2 ] ], "conv_kernel_sizes": [ [ 1, 3, 3 ], [ 1, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ] ], "unet_max_num_features": 320, "resampling_fn_data": "resample_data_or_seg_to_shape", "resampling_fn_seg": "resample_data_or_seg_to_shape", "resampling_fn_data_kwargs": { "is_seg": false, "order": 3, "order_z": 0, "force_separate_z": null }, "resampling_fn_seg_kwargs": { "is_seg": true, "order": 1, "order_z": 0, "force_separate_z": null }, "resampling_fn_probabilities": "resample_data_or_seg_to_shape", "resampling_fn_probabilities_kwargs": { "is_seg": false, "order": 1, "order_z": 0, "force_separate_z": null }, "batch_dice": false } }, "experiment_planner_used": "ExperimentPlanner", "label_manager": "LabelManager", "foreground_intensity_properties_per_channel": { "0": { "max": 2102.0, "mean": 575.5780639648438, "median": 516.0, "min": 157.0, "percentile_00_5": 241.0, "percentile_99_5": 1752.0, "std": 235.12808227539062 }, "1": { "max": 1.0, "mean": 1.0, "median": 1.0, "min": 1.0, "percentile_00_5": 1.0, "percentile_99_5": 1.0, "std": 0.0 } } } ```Dataset602_DCMlesionsMultiChannel
will be trained ondcm-zurich-lesions
anddcm-zurich-lesions-20231115
datasets using nnUNetv2.Manual lesion GTs are available for both datasets.
TODO