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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
Model: string
Checkpoint: string
HF_Repository: string
Device: string
Test_Patients: int64
Total_Parameters: int64
Trainable_Parameters: int64
Total_Inference_Seconds: double
Average_Inference_Seconds_Per_Patient: double
Mean_WT_Dice_Across_Patients: double
Mean_TC_Dice_Across_Patients: double
Mean_ET_Dice_Across_Patients: double
Mean_WT_HD95_Across_Patients: double
Mean_TC_HD95_Across_Patients: double
Mean_ET_HD95_Across_Patients: double
Mean_WT_Precision_Across_Patients: double
Mean_TC_Precision_Across_Patients: double
Mean_ET_Precision_Across_Patients: double
Mean_WT_Recall_Across_Patients: double
Mean_TC_Recall_Across_Patients: double
Mean_ET_Recall_Across_Patients: double
Mean_WT_FNR_Across_Patients: double
Mean_TC_FNR_Across_Patients: double
Mean_ET_FNR_Across_Patients: double
Mean_WT_FPR_Across_Patients: double
Mean_TC_FPR_Across_Patients: double
Mean_ET_FPR_Across_Patients: double
Mean_Dice: double
Mean_HD95: double
Mean_Recall: double
Mean_FNR: double
WT_Global_TP: int64
WT_Global_TN: int64
WT_Global_FP: int64
WT_Global_FN: int64
WT_Global_Dice: double
WT_Global_Recall: double
WT_Global_FNR: double
WT_Global_FPR: double
TC_Global_TP: int64
TC_Global_TN: int64
TC_Global_FP: int64
TC_Global_FN: int64
TC_Global_Dice: double
TC_Global_Recall: double
TC_Global_FNR: double
TC_Global_FPR: double
ET_Global_TP: int64
ET_Global_TN: int64
ET_Global_FP: int64
ET_Global_FN: int64
ET_Global_Dice: double
ET_Global_Recall: double
ET_Global_FNR: double
ET_Global_FPR: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 7790
to
{'Patient': Value('string'), 'Inference_Seconds': Value('float64'), 'WT_Dice': Value('float64'), 'WT_Precision': Value('float64'), 'WT_Recall': Value('float64'), 'WT_FNR': Value('float64'), 'WT_FPR': Value('float64'), 'WT_TP': Value('int64'), 'WT_TN': Value('int64'), 'WT_FP': Value('int64'), 'WT_FN': Value('int64'), 'WT_HD95': Value('float64'), 'TC_Dice': Value('float64'), 'TC_Precision': Value('float64'), 'TC_Recall': Value('float64'), 'TC_FNR': Value('float64'), 'TC_FPR': Value('float64'), 'TC_TP': Value('int64'), 'TC_TN': Value('int64'), 'TC_FP': Value('int64'), 'TC_FN': Value('int64'), 'TC_HD95': Value('float64'), 'ET_Dice': Value('float64'), 'ET_Precision': Value('float64'), 'ET_Recall': Value('float64'), 'ET_FNR': Value('float64'), 'ET_FPR': Value('float64'), 'ET_TP': Value('int64'), 'ET_TN': Value('int64'), 'ET_FP': Value('int64'), 'ET_FN': Value('int64'), 'ET_HD95': Value('float64'), 'Mean_Dice': Value('float64'), 'Mean_HD95': Value('float64'), 'Mean_Recall': Value('float64'), 'Mean_FNR': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1868, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 803, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
Model: string
Checkpoint: string
HF_Repository: string
Device: string
Test_Patients: int64
Total_Parameters: int64
Trainable_Parameters: int64
Total_Inference_Seconds: double
Average_Inference_Seconds_Per_Patient: double
Mean_WT_Dice_Across_Patients: double
Mean_TC_Dice_Across_Patients: double
Mean_ET_Dice_Across_Patients: double
Mean_WT_HD95_Across_Patients: double
Mean_TC_HD95_Across_Patients: double
Mean_ET_HD95_Across_Patients: double
Mean_WT_Precision_Across_Patients: double
Mean_TC_Precision_Across_Patients: double
Mean_ET_Precision_Across_Patients: double
Mean_WT_Recall_Across_Patients: double
Mean_TC_Recall_Across_Patients: double
Mean_ET_Recall_Across_Patients: double
Mean_WT_FNR_Across_Patients: double
Mean_TC_FNR_Across_Patients: double
Mean_ET_FNR_Across_Patients: double
Mean_WT_FPR_Across_Patients: double
Mean_TC_FPR_Across_Patients: double
Mean_ET_FPR_Across_Patients: double
Mean_Dice: double
Mean_HD95: double
Mean_Recall: double
Mean_FNR: double
WT_Global_TP: int64
WT_Global_TN: int64
WT_Global_FP: int64
WT_Global_FN: int64
WT_Global_Dice: double
WT_Global_Recall: double
WT_Global_FNR: double
WT_Global_FPR: double
TC_Global_TP: int64
TC_Global_TN: int64
TC_Global_FP: int64
TC_Global_FN: int64
TC_Global_Dice: double
TC_Global_Recall: double
TC_Global_FNR: double
TC_Global_FPR: double
ET_Global_TP: int64
ET_Global_TN: int64
ET_Global_FP: int64
ET_Global_FN: int64
ET_Global_Dice: double
ET_Global_Recall: double
ET_Global_FNR: double
ET_Global_FPR: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 7790
to
{'Patient': Value('string'), 'Inference_Seconds': Value('float64'), 'WT_Dice': Value('float64'), 'WT_Precision': Value('float64'), 'WT_Recall': Value('float64'), 'WT_FNR': Value('float64'), 'WT_FPR': Value('float64'), 'WT_TP': Value('int64'), 'WT_TN': Value('int64'), 'WT_FP': Value('int64'), 'WT_FN': Value('int64'), 'WT_HD95': Value('float64'), 'TC_Dice': Value('float64'), 'TC_Precision': Value('float64'), 'TC_Recall': Value('float64'), 'TC_FNR': Value('float64'), 'TC_FPR': Value('float64'), 'TC_TP': Value('int64'), 'TC_TN': Value('int64'), 'TC_FP': Value('int64'), 'TC_FN': Value('int64'), 'TC_HD95': Value('float64'), 'ET_Dice': Value('float64'), 'ET_Precision': Value('float64'), 'ET_Recall': Value('float64'), 'ET_FNR': Value('float64'), 'ET_FPR': Value('float64'), 'ET_TP': Value('int64'), 'ET_TN': Value('int64'), 'ET_FP': Value('int64'), 'ET_FN': Value('int64'), 'ET_HD95': Value('float64'), 'Mean_Dice': Value('float64'), 'Mean_HD95': Value('float64'), 'Mean_Recall': Value('float64'), 'Mean_FNR': Value('float64')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Patient string | Inference_Seconds float64 | WT_Dice float64 | WT_Precision float64 | WT_Recall float64 | WT_FNR float64 | WT_FPR float64 | WT_TP int64 | WT_TN int64 | WT_FP int64 | WT_FN int64 | WT_HD95 float64 | TC_Dice float64 | TC_Precision float64 | TC_Recall float64 | TC_FNR float64 | TC_FPR float64 | TC_TP int64 | TC_TN int64 | TC_FP int64 | TC_FN int64 | TC_HD95 float64 | ET_Dice float64 | ET_Precision float64 | ET_Recall float64 | ET_FNR float64 | ET_FPR float64 | ET_TP int64 | ET_TN int64 | ET_FP int64 | ET_FN int64 | ET_HD95 float64 | Mean_Dice float64 | Mean_HD95 float64 | Mean_Recall float64 | Mean_FNR float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BraTS20_Training_004 | 130.100366 | 0.93961 | 0.918514 | 0.961699 | 0.038301 | 0.001001 | 99,532 | 8,815,674 | 8,830 | 3,964 | 2 | 0.908445 | 0.954638 | 0.866515 | 0.133485 | 0.000122 | 22,876 | 8,900,513 | 1,087 | 3,524 | 2.828427 | 0.878083 | 0.856634 | 0.900632 | 0.099368 | 0.000262 | 13,958 | 8,910,166 | 2,336 | 1,540 | 1.414214 | 0.908713 | 2.08088 | 0.909615 | 0.090385 |
BraTS20_Training_013 | 132.4337 | 0.895734 | 0.8794 | 0.912687 | 0.087313 | 0.000653 | 42,293 | 8,875,861 | 5,800 | 4,046 | 3 | 0.90562 | 0.96865 | 0.850292 | 0.149708 | 0.000045 | 12,359 | 8,913,065 | 400 | 2,176 | 1.732051 | 0.684197 | 0.733636 | 0.641 | 0.359 | 0.000105 | 2,589 | 8,923,021 | 940 | 1,450 | 1.414214 | 0.828517 | 2.048755 | 0.801327 | 0.198673 |
BraTS20_Training_014 | 131.210368 | 0.881746 | 0.807103 | 0.971603 | 0.028397 | 0.00211 | 78,113 | 8,828,935 | 18,669 | 2,283 | 4.242641 | 0.873941 | 0.81185 | 0.946315 | 0.053685 | 0.000679 | 26,088 | 8,894,386 | 6,046 | 1,480 | 4.123106 | 0.823335 | 0.767222 | 0.888305 | 0.111695 | 0.000552 | 16,216 | 8,904,825 | 4,920 | 2,039 | 1 | 0.859674 | 3.121915 | 0.935407 | 0.064593 |
BraTS20_Training_016 | 136.127917 | 0.90052 | 0.848899 | 0.958825 | 0.041175 | 0.002466 | 121,952 | 8,779,104 | 21,707 | 5,237 | 3.162278 | 0.875805 | 0.945591 | 0.815613 | 0.184387 | 0.000217 | 33,507 | 8,884,990 | 1,928 | 7,575 | 3.162278 | 0.542944 | 0.373199 | 0.995933 | 0.004067 | 0.002445 | 12,978 | 8,893,172 | 21,797 | 53 | 4.898979 | 0.77309 | 3.741178 | 0.923457 | 0.076543 |
BraTS20_Training_017 | 131.395809 | 0.886168 | 0.806977 | 0.982592 | 0.017408 | 0.002683 | 99,004 | 8,803,561 | 23,681 | 1,754 | 5.09902 | 0.839483 | 0.841692 | 0.837287 | 0.162713 | 0.00026 | 12,319 | 8,910,970 | 2,317 | 2,394 | 5 | 0.828902 | 0.746067 | 0.932429 | 0.067571 | 0.000257 | 6,734 | 8,918,486 | 2,292 | 488 | 1 | 0.851518 | 3.699673 | 0.917436 | 0.082564 |
BraTS20_Training_045 | 132.353742 | 0.939613 | 0.916398 | 0.964035 | 0.035965 | 0.000968 | 93,709 | 8,822,246 | 8,549 | 3,496 | 2 | 0.941725 | 0.903758 | 0.983023 | 0.016977 | 0.000669 | 55,704 | 8,865,402 | 5,932 | 962 | 2 | 0.87762 | 0.817439 | 0.947367 | 0.052633 | 0.000955 | 37,997 | 8,879,406 | 8,486 | 2,111 | 1.414214 | 0.919653 | 1.804738 | 0.964808 | 0.035192 |
BraTS20_Training_048 | 130.810923 | 0.53108 | 0.964443 | 0.366428 | 0.633572 | 0.000044 | 10,714 | 8,898,366 | 395 | 18,525 | 19 | 0.84476 | 0.929265 | 0.774343 | 0.225657 | 0.000023 | 2,680 | 8,924,335 | 204 | 781 | 2 | 0.573942 | 0.92779 | 0.415483 | 0.584517 | 0.000007 | 848 | 8,925,893 | 66 | 1,193 | 2.44949 | 0.649927 | 7.816497 | 0.518751 | 0.481249 |
BraTS20_Training_053 | 132.406246 | 0.945661 | 0.921821 | 0.970767 | 0.029233 | 0.001406 | 145,515 | 8,765,762 | 12,341 | 4,382 | 2.236068 | 0.839716 | 0.970554 | 0.739963 | 0.260037 | 0.000054 | 15,887 | 8,906,048 | 482 | 5,583 | 5 | 0.841466 | 0.843048 | 0.83989 | 0.16011 | 0.000159 | 7,622 | 8,917,506 | 1,419 | 1,453 | 1 | 0.875614 | 2.745356 | 0.850206 | 0.149794 |
BraTS20_Training_058 | 129.163524 | 0.676225 | 0.885203 | 0.547072 | 0.452928 | 0.000736 | 50,137 | 8,829,852 | 6,502 | 41,509 | 11.575837 | 0.670905 | 0.807183 | 0.573997 | 0.426003 | 0.000265 | 9,867 | 8,908,453 | 2,357 | 7,323 | 11.401754 | 0.555224 | 0.648794 | 0.485242 | 0.514758 | 0.000198 | 3,255 | 8,919,530 | 1,762 | 3,453 | 13.601471 | 0.634118 | 12.193021 | 0.535437 | 0.464563 |
BraTS20_Training_072 | 137.87907 | 0.763821 | 0.66882 | 0.890278 | 0.109722 | 0.004448 | 79,403 | 8,799,493 | 39,318 | 9,786 | 7.81025 | 0.938781 | 0.960977 | 0.917588 | 0.082412 | 0.000203 | 44,425 | 8,877,781 | 1,804 | 3,990 | 2.44949 | 0.88808 | 0.880705 | 0.89558 | 0.10442 | 0.000513 | 33,672 | 8,885,841 | 4,561 | 3,926 | 2 | 0.863561 | 4.08658 | 0.901148 | 0.098852 |
BraTS20_Training_080 | 134.551193 | 0.890818 | 0.877612 | 0.904427 | 0.095573 | 0.000617 | 39,310 | 8,879,054 | 5,482 | 4,154 | 2.828427 | 0.83776 | 0.885874 | 0.794602 | 0.205398 | 0.000121 | 8,391 | 8,916,359 | 1,081 | 2,169 | 3 | 0.819799 | 0.830038 | 0.809809 | 0.190191 | 0.000124 | 5,416 | 8,920,203 | 1,109 | 1,272 | 2 | 0.849459 | 2.609476 | 0.836279 | 0.163721 |
BraTS20_Training_082 | 138.638626 | 0.788908 | 0.856894 | 0.730918 | 0.269082 | 0.000138 | 7,383 | 8,916,666 | 1,233 | 2,718 | 5 | 0.847046 | 0.957166 | 0.759651 | 0.240349 | 0.000024 | 4,782 | 8,921,491 | 214 | 1,513 | 2.236068 | 0.726093 | 0.904024 | 0.606684 | 0.393316 | 0.000033 | 2,741 | 8,923,191 | 291 | 1,777 | 1.414214 | 0.787349 | 2.883427 | 0.699084 | 0.300916 |
BraTS20_Training_102 | 128.381415 | 0.904966 | 0.881729 | 0.929462 | 0.070538 | 0.000338 | 22,440 | 8,900,847 | 3,010 | 1,703 | 2.828427 | 0.907673 | 0.979188 | 0.845894 | 0.154106 | 0.00003 | 12,515 | 8,912,939 | 266 | 2,280 | 2 | 0.807575 | 0.933781 | 0.711422 | 0.288578 | 0.000042 | 5,288 | 8,920,192 | 375 | 2,145 | 1.414214 | 0.873405 | 2.08088 | 0.828926 | 0.171074 |
BraTS20_Training_111 | 128.634773 | 0.922724 | 0.877008 | 0.973467 | 0.026533 | 0.000666 | 42,192 | 8,878,741 | 5,917 | 1,150 | 2 | 0.952551 | 0.950851 | 0.954258 | 0.045742 | 0.000144 | 24,763 | 8,900,770 | 1,280 | 1,187 | 1 | 0.905207 | 0.918355 | 0.89243 | 0.10757 | 0.000156 | 15,680 | 8,909,036 | 1,394 | 1,890 | 1 | 0.926827 | 1.333333 | 0.940052 | 0.059948 |
BraTS20_Training_112 | 133.342272 | 0.954027 | 0.927125 | 0.982537 | 0.017463 | 0.001502 | 167,384 | 8,744,484 | 13,157 | 2,975 | 1.414214 | 0.979375 | 0.970526 | 0.988388 | 0.011612 | 0.000232 | 67,667 | 8,857,483 | 2,055 | 795 | 1 | 0.956816 | 0.952837 | 0.960828 | 0.039172 | 0.000258 | 46,285 | 8,877,537 | 2,291 | 1,887 | 1 | 0.963406 | 1.138071 | 0.977251 | 0.022749 |
BraTS20_Training_113 | 131.10821 | 0.94882 | 0.911801 | 0.988972 | 0.011028 | 0.001366 | 124,294 | 8,790,297 | 12,023 | 1,386 | 9.643651 | 0.606332 | 0.484538 | 0.809914 | 0.190086 | 0.000637 | 5,343 | 8,915,719 | 5,684 | 1,254 | 5.300109 | 0.747832 | 0.735334 | 0.760762 | 0.239238 | 0.00013 | 3,234 | 8,922,585 | 1,164 | 1,017 | 12.083046 | 0.767661 | 9.008935 | 0.853216 | 0.146784 |
BraTS20_Training_115 | 136.054859 | 0.956461 | 0.932944 | 0.981194 | 0.018806 | 0.000723 | 88,904 | 8,831,002 | 6,390 | 1,704 | 1.414214 | 0.97638 | 0.980773 | 0.972025 | 0.027975 | 0.000042 | 19,180 | 8,907,892 | 376 | 552 | 1 | 0.95459 | 0.970865 | 0.93885 | 0.06115 | 0.000045 | 13,296 | 8,913,439 | 399 | 866 | 1 | 0.962477 | 1.138071 | 0.964023 | 0.035977 |
BraTS20_Training_120 | 139.818277 | 0.919733 | 0.856232 | 0.993408 | 0.006592 | 0.002342 | 122,817 | 8,783,746 | 20,622 | 815 | 44.952196 | 0.959844 | 0.974945 | 0.945203 | 0.054797 | 0.000144 | 49,574 | 8,874,278 | 1,274 | 2,874 | 1.414214 | 0.854799 | 0.93987 | 0.78385 | 0.21615 | 0.00011 | 15,318 | 8,907,478 | 980 | 4,224 | 1 | 0.911459 | 15.788803 | 0.907487 | 0.092513 |
BraTS20_Training_126 | 138.719913 | 0.906921 | 0.88048 | 0.935 | 0.065 | 0.000543 | 35,530 | 8,885,177 | 4,823 | 2,470 | 3.464102 | 0.880819 | 0.828108 | 0.940696 | 0.059304 | 0.000432 | 18,543 | 8,904,439 | 3,849 | 1,169 | 3.316625 | 0.893912 | 0.889284 | 0.898588 | 0.101412 | 0.000203 | 14,514 | 8,910,041 | 1,807 | 1,638 | 1.414214 | 0.893884 | 2.731647 | 0.924761 | 0.075239 |
BraTS20_Training_141 | 134.438317 | 0.883372 | 0.940304 | 0.83294 | 0.16706 | 0.000128 | 18,004 | 8,905,242 | 1,143 | 3,611 | 6.324555 | 0 | 0 | 0 | 1 | 0 | 0 | 8,918,879 | 0 | 9,121 | null | 0 | 0 | 0 | 1 | 0 | 0 | 8,927,292 | 0 | 708 | null | 0.294457 | 6.324555 | 0.277647 | 0.722353 |
BraTS20_Training_143 | 132.870459 | 0.924499 | 0.918165 | 0.93092 | 0.06908 | 0.001397 | 137,577 | 8,767,952 | 12,262 | 10,209 | 3.162278 | 0.950275 | 0.978029 | 0.924051 | 0.075949 | 0.000293 | 114,672 | 8,801,327 | 2,576 | 9,425 | 2.236068 | 0.736988 | 0.935962 | 0.607781 | 0.392219 | 0.00034 | 43,979 | 8,852,631 | 3,009 | 28,381 | 2.44949 | 0.870587 | 2.615945 | 0.820917 | 0.179083 |
BraTS20_Training_175 | 135.534974 | 0.926995 | 0.9281 | 0.925893 | 0.074107 | 0.001532 | 172,854 | 8,727,920 | 13,391 | 13,835 | 6.63325 | 0.917716 | 0.870326 | 0.970565 | 0.029435 | 0.001556 | 92,225 | 8,819,237 | 13,741 | 2,797 | 3.605551 | 0.88079 | 0.814883 | 0.958296 | 0.041704 | 0.001944 | 75,714 | 8,831,791 | 17,200 | 3,295 | 2.236068 | 0.9085 | 4.15829 | 0.951584 | 0.048416 |
BraTS20_Training_215 | 134.82038 | 0.890319 | 0.81688 | 0.978267 | 0.021733 | 0.00165 | 65,223 | 8,846,707 | 14,621 | 1,449 | 2.44949 | 0.912752 | 0.963661 | 0.866952 | 0.133048 | 0.000132 | 31,212 | 8,890,821 | 1,177 | 4,790 | 2.828427 | 0.850558 | 0.942019 | 0.775285 | 0.224715 | 0.000139 | 20,114 | 8,900,818 | 1,238 | 5,830 | 1.414214 | 0.884543 | 2.23071 | 0.873501 | 0.126499 |
BraTS20_Training_217 | 132.152975 | 0.928771 | 0.944337 | 0.91371 | 0.08629 | 0.000133 | 20,087 | 8,904,832 | 1,184 | 1,897 | 1.732051 | 0.9261 | 0.940075 | 0.912535 | 0.087465 | 0.000078 | 10,840 | 8,915,430 | 691 | 1,039 | 1.414214 | 0.916892 | 0.928355 | 0.905707 | 0.094293 | 0.000069 | 7,982 | 8,918,571 | 616 | 831 | 1 | 0.923921 | 1.382088 | 0.910651 | 0.089349 |
BraTS20_Training_230 | 133.426573 | 0.952234 | 0.917846 | 0.989299 | 0.010701 | 0.000909 | 89,769 | 8,829,225 | 8,035 | 971 | 1.414214 | 0.966202 | 0.954466 | 0.97823 | 0.02177 | 0.000082 | 15,323 | 8,911,605 | 731 | 341 | 1 | 0.968191 | 0.946057 | 0.991385 | 0.008615 | 0.000096 | 14,960 | 8,912,057 | 853 | 130 | 1 | 0.962209 | 1.138071 | 0.986305 | 0.013695 |
BraTS20_Training_259 | 132.846877 | 0.952226 | 0.953074 | 0.95138 | 0.04862 | 0.000634 | 113,513 | 8,803,097 | 5,589 | 5,801 | 4.055398 | 0.546897 | 0.385049 | 0.943469 | 0.056531 | 0.00229 | 12,784 | 8,894,033 | 20,417 | 766 | 20.34699 | 0.856091 | 0.782872 | 0.944419 | 0.055581 | 0.000322 | 10,348 | 8,914,173 | 2,870 | 609 | 2.236068 | 0.785072 | 8.879485 | 0.946423 | 0.053577 |
BraTS20_Training_280 | 135.674024 | 0.869278 | 0.800949 | 0.950353 | 0.049647 | 0.001043 | 37,289 | 8,879,496 | 9,267 | 1,948 | 96.183158 | 0.742942 | 0.961538 | 0.605327 | 0.394673 | 0.000002 | 500 | 8,927,154 | 20 | 326 | 2 | 0.782972 | 0.925049 | 0.678726 | 0.321274 | 0.000004 | 469 | 8,927,271 | 38 | 222 | 1.414214 | 0.798397 | 33.199124 | 0.744802 | 0.255198 |
BraTS20_Training_288 | 133.135684 | 0.8018 | 0.957803 | 0.689498 | 0.310502 | 0.000264 | 53,092 | 8,848,660 | 2,339 | 23,909 | 5.91608 | 0.788071 | 0.884088 | 0.710868 | 0.289132 | 0.00039 | 26,413 | 8,887,381 | 3,463 | 10,743 | 6.708204 | 0.858257 | 0.916988 | 0.806596 | 0.193404 | 0.00015 | 14,747 | 8,908,382 | 1,335 | 3,536 | 1.732051 | 0.816043 | 4.785445 | 0.735654 | 0.264346 |
BraTS20_Training_302 | 129.856244 | 0.631319 | 0.999251 | 0.46142 | 0.53858 | 0.000005 | 53,348 | 8,812,343 | 40 | 62,269 | 12.041595 | 0.44137 | 0.986948 | 0.284243 | 0.715757 | 0.000044 | 29,113 | 8,825,192 | 385 | 73,310 | 16.155494 | 0.784722 | 0.64864 | 0.993062 | 0.006938 | 0.00013 | 2,147 | 8,924,675 | 1,163 | 15 | 2.236068 | 0.619137 | 10.144386 | 0.579575 | 0.420425 |
BraTS20_Training_303 | 132.676531 | 0.35427 | 0.930809 | 0.218767 | 0.781233 | 0.000066 | 7,843 | 8,891,566 | 583 | 28,008 | 15.684387 | 0 | 0 | 0 | 1 | 0 | 0 | 8,902,802 | 0 | 25,198 | null | 0 | 0 | 0 | 1 | 0 | 0 | 8,926,676 | 0 | 1,324 | null | 0.11809 | 15.684387 | 0.072922 | 0.927078 |
BraTS20_Training_309 | 130.837552 | 0.822217 | 0.992652 | 0.701733 | 0.298267 | 0.000093 | 110,365 | 8,769,908 | 817 | 46,910 | 8.774964 | 0.16261 | 0.999893 | 0.088501 | 0.911499 | 0 | 9,342 | 8,822,441 | 1 | 96,216 | 18.788294 | 0.687679 | 0.650995 | 0.728745 | 0.271255 | 0.000086 | 1,440 | 8,925,252 | 772 | 536 | 18.05547 | 0.557502 | 15.206243 | 0.506326 | 0.493674 |
BraTS20_Training_328 | 134.186248 | 0.928181 | 0.950791 | 0.906621 | 0.093379 | 0.001982 | 328,000 | 8,549,241 | 16,976 | 33,783 | 6.403124 | 0.010537 | 0.884987 | 0.0053 | 0.9947 | 0.000015 | 1,008 | 8,737,681 | 131 | 189,180 | 47.265209 | 0.391886 | 0.37511 | 0.410232 | 0.589768 | 0.000079 | 425 | 8,926,256 | 708 | 611 | 51.134625 | 0.443534 | 34.934319 | 0.440717 | 0.559283 |
BraTS20_Training_333 | 133.658169 | 0.963861 | 0.982868 | 0.945576 | 0.054424 | 0.000356 | 178,417 | 8,736,204 | 3,110 | 10,269 | 2.236068 | 0.003982 | 0.791946 | 0.001996 | 0.998004 | 0.000011 | 354 | 8,750,546 | 93 | 177,007 | 31.921779 | 0.069431 | 0.735023 | 0.036436 | 0.963564 | 0.000013 | 319 | 8,919,130 | 115 | 8,436 | 16.757084 | 0.345758 | 16.971644 | 0.328003 | 0.671997 |
BraTS20_Training_339 | 133.263049 | 0.921545 | 0.933855 | 0.909555 | 0.090445 | 0.001081 | 134,053 | 8,771,122 | 9,495 | 13,330 | 4.242641 | 0.952555 | 0.936597 | 0.969066 | 0.030934 | 0.000474 | 62,028 | 8,859,793 | 4,199 | 1,980 | 2 | 0.910949 | 0.902228 | 0.91984 | 0.08016 | 0.000432 | 35,435 | 8,885,637 | 3,840 | 3,088 | 1 | 0.92835 | 2.414214 | 0.932821 | 0.067179 |
BraTS20_Training_347 | 134.515483 | 0.961859 | 0.933886 | 0.99156 | 0.00844 | 0.000889 | 110,672 | 8,808,551 | 7,835 | 942 | 1.414214 | 0.967218 | 0.952073 | 0.982852 | 0.017148 | 0.000181 | 31,983 | 8,893,849 | 1,610 | 558 | 1 | 0.951763 | 0.950752 | 0.952777 | 0.047223 | 0.000138 | 23,707 | 8,901,890 | 1,228 | 1,175 | 1 | 0.96028 | 1.138071 | 0.97573 | 0.02427 |
BraTS20_Training_357 | 138.4952 | 0.878148 | 0.88496 | 0.871439 | 0.128561 | 0.000758 | 51,733 | 8,861,910 | 6,725 | 7,632 | 57.567352 | 0.892323 | 0.922748 | 0.86384 | 0.13616 | 0.000201 | 21,393 | 8,901,444 | 1,791 | 3,372 | 3 | 0.845475 | 0.899731 | 0.797389 | 0.202611 | 0.000147 | 11,728 | 8,911,985 | 1,307 | 2,980 | 3 | 0.871982 | 21.189117 | 0.844223 | 0.155777 |
BraTS20_Training_360 | 133.528221 | 0.914472 | 0.849285 | 0.990499 | 0.009501 | 0.001961 | 97,576 | 8,812,172 | 17,316 | 936 | 4.242641 | 0.951437 | 0.919703 | 0.98544 | 0.01456 | 0.000548 | 55,700 | 8,866,614 | 4,863 | 823 | 2 | 0.929844 | 0.893114 | 0.969724 | 0.030276 | 0.000572 | 42,439 | 8,879,157 | 5,079 | 1,325 | 1 | 0.931918 | 2.414214 | 0.981887 | 0.018113 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 0.787043 | 7.212474 | 0.786442 | 0.213558 |
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