Project-MONAI / Project-MONAI/MONAILabel

Only one channel is recognized out of 15 channels

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Python
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Forks
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15h 41m
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Description

[2024-03-10 14:28:38,126] [18300] [MainThread] [INFO] (main:61) - Result: {"rank": 0, "current_epoch": 2000, "current_iteration": 24000, "total_epochs": 2000, "total_iterations": 12, "epoch": 2000, "start_ts": 1710020807, "total_time": "8:41:50", "best_metric": 0.787301242351532, "train": {"metrics": {"train_mean_dice": 0.8769383430480957, "train_nodule_mean_dice": 0.0, "train_bone_mean_dice": 0.0, "train_aorta_mean_dice": 0.0, "train_pulmonary_artery_mean_dice": 0.0, "train_pulmonary_vein_mean_dice": 0.0, "train_vein_mean_dice": 0.0, "train_trachea_mean_dice": 0.0, "train_esophagus_mean_dice": 0.0, "train_heart_mean_dice": 0.0, "train_lung_upper_lobe_left_mean_dice": 0.0, "train_lung_lower_lobe_left_mean_dice": 0.0, "train_lung_upper_lobe_right_mean_dice": 0.0, "train_lung_middle_lobe_right_mean_dice": 0.0, "train_lung_lower_lobe_right_mean_dice": 0.0, "train_skin_mean_dice": 0.9323155283927917}, "key_metric_name": "train_mean_dice", "best_metric": 0.9949172139167786, "best_metric_epoch": 1851}, "eval": {"metrics": {"val_mean_dice": 0.7803362011909485, "val_nodule_mean_dice": 0.0, "val_bone_mean_dice": 0.0, "val_aorta_mean_dice": 0.0, "val_pulmonary_artery_mean_dice": 0.0, "val_pulmonary_vein_mean_dice": 0.0, "val_vein_mean_dice": 0.0, "val_trachea_mean_dice": 0.0, "val_esophagus_mean_dice": 0.0, "val_heart_mean_dice": 0.0, "val_lung_upper_lobe_left_mean_dice": 0.0, "val_lung_lower_lobe_left_mean_dice": 0.0, "val_lung_upper_lobe_right_mean_dice": 0.0, "val_lung_middle_lobe_right_mean_dice": 0.0, "val_lung_lower_lobe_right_mean_dice": 0.0, "val_skin_mean_dice": 0.9444830417633057}, "key_metric_name": "val_mean_dice", "best_metric": 0.787301242351532, "best_metric_epoch": 1669}}
[2024-03-10 14:28:39,100] [13122] [ThreadPoolExecutor-1_0] [INFO] (monailabel.utils.async_tasks.utils:83) - Return code: 0
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First steps

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Research direction

The report provides training logs and screenshots, but no file, test, or entry point is identified. Start by reproducing the run that reports nonzero metrics only for the skin channel and trace how the 15 channels are configured and evaluated. Done means identifying the cause and verifying that each expected channel is recognized and produces metrics.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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