add Dataset201_MSWAL

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

Dataset201_MSWAL/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/dataset.json

@@ -0,0 +1,2803 @@
+{
+    "name": "MSWAL",
+    "description": " 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset",
+    "licence": "CC BY-NC 4.0",
+    "relase": "July 8, 2025",
+    "tensorImageSize": "3D",
+    "file_ending": ".nii.gz",
+    "channel_names": {
+        "0": "CT"
+    },
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+        "background": 0,
+        "gallstone": 1,
+        "kidney stone": 2,
+        "liver tumor": 3,
+        "kidney tumor": 4,
+        "pancreatic cancer": 5,
+        "liver cyst": 6,
+        "kidney cyst": 7
+    },
+    "numTraining": 484,
+    "numTest": 210,
+    "training": [
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+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2026-04-08 15:26:21.098278: do_dummy_2d_data_aug: False 
+2026-04-08 15:26:21.143579: Creating new 5-fold cross-validation split... 
+2026-04-08 15:26:21.152699: Desired fold for training: 0 
+2026-04-08 15:26:21.155444: This split has 387 training and 97 validation cases. 

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+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2026-04-08 15:27:18.494670: do_dummy_2d_data_aug: False 
+2026-04-08 15:27:18.498723: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json 
+2026-04-08 15:27:18.502337: The split file contains 5 splits. 
+2026-04-08 15:27:18.504030: Desired fold for training: 1 
+2026-04-08 15:27:18.505772: This split has 387 training and 97 validation cases. 

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+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2026-04-08 15:28:32.557918: do_dummy_2d_data_aug: False 
+2026-04-08 15:28:32.605994: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json 
+2026-04-08 15:28:32.609672: The split file contains 5 splits. 
+2026-04-08 15:28:32.611791: Desired fold for training: 2 
+2026-04-08 15:28:32.613461: This split has 387 training and 97 validation cases. 

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+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2026-04-08 15:29:13.407142: do_dummy_2d_data_aug: False 
+2026-04-08 15:29:13.412213: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json 
+2026-04-08 15:29:13.414359: The split file contains 5 splits. 
+2026-04-08 15:29:13.415981: Desired fold for training: 3 
+2026-04-08 15:29:13.417588: This split has 387 training and 97 validation cases. 

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+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2026-04-08 15:31:10.522146: do_dummy_2d_data_aug: False 
+2026-04-08 15:31:10.527774: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json 
+2026-04-08 15:31:10.531223: The split file contains 5 splits. 
+2026-04-08 15:31:10.533201: Desired fold for training: 4 
+2026-04-08 15:31:10.536277: This split has 388 training and 96 validation cases. 

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+                        [
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+                            3,
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+                            1,
+                            1
+                        ],
+                        [
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+                            2,
+                            2
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+                        [
+                            2,
+                            2,
+                            2
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+                        [
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+                        [
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+                            2,
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+                        [
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+                            2,
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+                        4,
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+                        6,
+                        6,
+                        6
+                    ],
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+                        1,
+                        1,
+                        1,
+                        1,
+                        1
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+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
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+                        "affine": true
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+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "nnUNetPlannerResEncL",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3071.0,
+            "mean": 71.96339416503906,
+            "median": 45.0,
+            "min": -932.0,
+            "percentile_00_5": -93.0,
+            "percentile_99_5": 1052.0,
+            "std": 141.6230926513672
+        }
+    }
+}