Image segmentations produced by BAMF under the AIMI Annotations initiative Article Swipe

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Ujjwal Baid , Satyam Ghodasara , Suyash Mohan , Michel Bilello , Evan Calabrese , Errol Colak , Keyvan Farahani , Jayashree Kalpathy-Cramer , Felipe Kitamura , Sarthak Pati , Luciano M. Prevedello , Jeffrey D. Rudie , Chiharu Sako , Russell T. Shinohara , Timothy Bergquist , Rong Chai , J. Mark Eddy , Julia Elliott , Walter Reade , Thomas Schaffter , Thomas Yu , Jiaxin Zheng , Ahmed W. Moawad , Luiz Otavio Coelho , Olivia McDonnell , Elka Miller , Fanny Morón , Mark Oswood , Robert Shih , Loizos Siakallis , Yulia Bronstein , James R. Mason , Anthony F. Miller , Gagandeep Choudhary , Aanchal Agarwal , Cristina Besada , Jamal J. Derakhshan , M.C. Diogo , Daniel D. Do‐Dai , Luciano Farage , John L. Go , Mohiuddin Hadi , Virginia Hill , Michael Iv , David Joyner , Christie M. Lincoln , Eyal Lotan , Asako Miyakoshi , Mariana Sanchez-Montano , Jaya Nath , Xuan V. Nguyen , Manal Nicolas‐Jilwan , Johanna Ortiz Jiménez , Kerem Öztürk , Bojan Petrović , Chintan Shah , Lubdha M. Shah , Manas Sharma , Onur Simsek , Achint K. Singh , Salil Soman , Volodymyr Statsevych , Brent D. Weinberg , Robert J. Young , Ichiro Ikuta , Amit Agarwal , Sword C. Cambron , Richard Silbergleit , Alexandru Dusoi , Alida A. Postma , Laurent Létourneau‐Guillon , Gloria Guzmán , Atin Saha , Neetu Soni , Greg Zaharchuk , Vahe M. Zohrabian , Yingming Chen , Miloš Cekić , AKM Fazlur Rahman , Juan E. Small , Varun Sethi , Christos Davatzikos , John Mongan , Christopher P. Hess , Soonmee Cha , Javier Villanueva-Meyer , John Freymann , Justin Kirby , Benedikt Wiestler , Priscila Crivellaro , Rivka R. Colen , Aikaterini Kotrotsou , Daniel C. Marcus , Mikhail Milchenko , Arash Nazeri , Hassan M. Fathallah‐Shaykh , Roland Wiest , András Jakab , Marc‐André Weber , Abhishek Mahajan ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.5281/zenodo.8345959

The Imaging Data Commons (IDC)(https://imaging.datacommons.cancer.gov/) [1] connects researchers with publicly available cancer imaging data, often linked with other types of cancer data. Many of the collections have limited annotations due to the expense and effort required to create these manually. The increased capabilities of AI analysis of radiology images provide an opportunity to augment existing IDC collections with new annotation data. To further this goal, we trained several nnUNet [2] based models for a variety of radiology segmentation tasks from public datasets and used them to generate segmentations for IDC collections. To validate the model's performance, roughly 10% of the AI predictions were assigned to a validation set. For this set, a board-certified radiologist graded the quality of AI predictions on a Likert scale. If they did not 'strongly agree' with the AI output, the reviewer corrected the segmentation. This record provides the AI segmentations, Manually corrected segmentations, and Manual scores for the inspected IDC Collection images. Only 10% of the AI-derived annotations provided in this dataset are verified by expert radiologists . More details, on model training and annotations are provided within the associated manuscript to ensure transparency and reproducibility. This work was done in two stages. Versions 1.x of this record were from the first stage. Versions 2.x added additional records. In the Version 1.x collections, a medical student (non-expert) reviewed all the AI predictions and rated them on a 5-point Likert Scale, for any AI predictions in the validation set that they did not 'strongly agree' with, the non-expert provided corrected segmentations. This non-expert was not utilized for the Version 2.x additional records. Likert Score Definition: Guidelines for reviewers to grade the quality of AI segmentations. 5 Strongly Agree - Use-as-is (i.e., clinically acceptable, and could be used for treatment without change) 4 Agree - Minor edits that are not necessary. Stylistic differences, but not clinically important. The current segmentation is acceptable 3 Neither agree nor disagree - Minor edits that are necessary. Minor edits are those that the review judges can be made in less time than starting from scratch or are expected to have minimal effect on treatment outcome 2 Disagree - Major edits. This category indicates that the necessary edit is required to ensure correctness, and sufficiently significant that user would prefer to start from the scratch 1 Strongly disagree - Unusable. This category indicates that the quality of the automatic annotations is so bad that they are unusable. Zip File Folder Structure Each zip file in the collection correlates to a specific segmentation task. The common folder structure is ai-segmentations-dcm This directory contains the AI model predictions in DICOM-SEG format for all analyzed IDC collection files qa-segmentations-dcm This directory contains manual corrected segmentation files, based on the AI prediction, in DICOM-SEG format. Only a fraction, ~10%, of the AI predictions were corrected. Corrections were performed by radiologist (rad*) and non-experts (ne*) qa-results.csv CSV file linking the study/series UIDs with the ai segmentation file, radiologist corrected segmentation file, radiologist ratings of AI performance. qa-results.csv Columns The qa-results.csv file contains metadata about the segmentations, their related IDC case image, as well as the Likert ratings and comments by the reviewers. Column Description Collection The name of the IDC collection for this case PatientID PatientID in DICOM metadata of scan. Also called Case ID in the IDC StudyInstanceUID StudyInstanceUID in the DICOM metadata of the scan SeriesInstanceUID SeriesInstanceUID in the DICOM metadata of the scan Validation true/false if this scan was manually reviewed Reviewer Coded ID of the reviewer. Radiologist IDs start with ‘rad’ non-expect IDs start with ‘ne’ AimiProjectYear 2023 or 2024, This work was split over two years. The main methodology difference between the two is that in 2023, a non-expert also reviewed the AI output, but a non-expert was not utilized in 2024. AISegmentation The filename of the AI prediction file in DICOM-seg format. This file is in the ai-segmentations-dcm folder. CorrectedSegmentation The filename of the reviewer-corrected prediction file in DICOM-seg format. This file is in the qa-segmentations-dcm folder. If the reviewer strongly agreed with the AI for all segments, they did not provide any correction file. Was the AI predicted ROIs accurate? This column appears one for each segment in the task for images from AimiProjectYear 2023. The reviewer rates segmentation quality on a Likert scale. In tasks that have multiple labels in the output, there is only one rating to cover them all. Was the AI predicted {SEGMENT_NAME} label accurate? This column appears one for each segment in the task for images from AimiProjectYear 2024. The reviewer rates each segment for its quality on a Likert scale. Do you have any comments about the AI predicted ROIs? Open ended question for the reviewer Do you have any comments about the findings from the study scans? Open ended question for the reviewer File Overview brain-mr.zip Segment Description: brain tumor regions: necrosis, edema, enhancing IDC Collection: UPENN-GBM Links: model weights, github breast-fdg-pet-ct.zip Segment Description: FDG-avid lesions in breast from FDG PET/CT scans QIN-Breast IDC Collection: QIN-Breast Links: model weights, github breast-mr.zip Segment Description: Breast, Fibroglandular tissue, structural tumor IDC Collection: duke-breast-cancer-mri Links: model weights, github kidney-ct.zip Segment Description: Kidney, Tumor, and Cysts from contrast enhanced CT scans IDS Collection: TCGA-KIRC, TCGA-KIRP, TCGA-KICH, CPTAC-CCRCC Links: model weights, github liver-ct.zip Segment Description: Liver from CT scans IDC Collection: TCGA-LIHC Links: model weights, github liver2-ct.zip Segment Description: Liver and Lesions from CT scans IDC Collection: HCC-TACE-SEG, COLORECTAL-LIVER-METASTASES Links: model weights, github liver-mr.zip Segment Description: Liver from T1 MRI scans IDC Collection: TCGA-LIHC Links: model weights, github lung-ct.zip Segment Description: Lung and Nodules (3mm-30mm) from CT scans IDC Collections: Anti-PD-1-Lung LUNG-PET-CT-Dx NSCLC Radiogenomics RIDER Lung PET-CT TCGA-LUAD TCGA-LUSC Links: model weights 1, model weights 2, github lung2-ct.zip Improved model version Segment Description: Lung and Nodules (3mm-30mm) from CT scans IDC Collections: QIN-LUNG-CT, SPIE-AAPM Lung CT Challenge Links: model weights, github lung-fdg-pet-ct.zip Segment Description: Lungs and FDG-avid lesions in the lung from FDG PET/CT scans IDC Collections: ACRIN-NSCLC-FDG-PET Anti-PD-1-Lung LUNG-PET-CT-Dx NSCLC Radiogenomics RIDER Lung PET-CT TCGA-LUAD TCGA-LUSC Links: model weights, github prostate-mr.zip Segment Description: Prostate from T2 MRI scans IDC Collection: ProstateX, Prostate-MRI-US-Biopsy Links: model weights, github Changelog 2.0.2 - Fix the brain-mr segmentations to be transformed correctly 2.0.1 - added AIMI 2024 radiologist comments to qa-results.csv 2.0.0 - added AIMI 2024 segmentations 1.X - AIMI 2023 segmentations and reviewer scores

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Brain Tumor

Computer Science

Benchmark (Surveying)

Artificial Intelligence

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Benchmarking Medical physics Brain tumor Computer science Benchmark (surveying) Artificial intelligence Medicine Pathology Management Geodesy Economics Geography

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2107.02314
PDF: https://arxiv.org/pdf/2107.02314
OA Status: green
Cited By: 356
References: 2
Related Works: 10
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DOI: https://doi.org/10.5281/zenodo.8345959

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Title: Image segmentations produced by BAMF under the AIMI Annotations initiative

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Publication year: 2024

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Publication date: 2024-08-06

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Authors: Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe Kitamura, Sarthak Pati, Luciano M. Prevedello, Jeffrey D. Rudie, Chiharu Sako, Russell T. Shinohara, Timothy Bergquist, Rong Chai, J. Mark Eddy, Julia Elliott, Walter Reade, Thomas Schaffter, Thomas Yu, Jiaxin Zheng, Ahmed W. Moawad, Luiz Otavio Coelho, Olivia McDonnell, Elka Miller, Fanny Morón, Mark Oswood, Robert Shih, Loizos Siakallis, Yulia Bronstein, James R. Mason, Anthony F. Miller, Gagandeep Choudhary, Aanchal Agarwal, Cristina Besada, Jamal J. Derakhshan, M.C. Diogo, Daniel D. Do‐Dai, Luciano Farage, John L. Go, Mohiuddin Hadi, Virginia Hill, Michael Iv, David Joyner, Christie M. Lincoln, Eyal Lotan, Asako Miyakoshi, Mariana Sanchez-Montano, Jaya Nath, Xuan V. Nguyen, Manal Nicolas‐Jilwan, Johanna Ortiz Jiménez, Kerem Öztürk, Bojan Petrović, Chintan Shah, Lubdha M. Shah, Manas Sharma, Onur Simsek, Achint K. Singh, Salil Soman, Volodymyr Statsevych, Brent D. Weinberg, Robert J. Young, Ichiro Ikuta, Amit Agarwal, Sword C. Cambron, Richard Silbergleit, Alexandru Dusoi, Alida A. Postma, Laurent Létourneau‐Guillon, Gloria Guzmán, Atin Saha, Neetu Soni, Greg Zaharchuk, Vahe M. Zohrabian, Yingming Chen, Miloš Cekić, AKM Fazlur Rahman, Juan E. Small, Varun Sethi, Christos Davatzikos, John Mongan, Christopher P. Hess, Soonmee Cha, Javier Villanueva-Meyer, John Freymann, Justin Kirby, Benedikt Wiestler, Priscila Crivellaro, Rivka R. Colen, Aikaterini Kotrotsou, Daniel C. Marcus, Mikhail Milchenko, Arash Nazeri, Hassan M. Fathallah‐Shaykh, Roland Wiest, András Jakab, Marc‐André Weber, Abhishek Mahajan

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Concepts: Benchmarking, Medical physics, Brain tumor, Computer science, Benchmark (surveying), Artificial intelligence, Medicine, Pathology, Management, Geodesy, Economics, Geography

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abstract_inverted_index.file	411, 480, 503, 637, 642, 655, 660
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abstract_inverted_index.have	26, 348, 717, 768, 784
abstract_inverted_index.less	338
abstract_inverted_index.lung	982
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abstract_inverted_index.main	605
abstract_inverted_index.name	529
abstract_inverted_index.only	725
abstract_inverted_index.over	601
abstract_inverted_index.scan	559, 568, 573
abstract_inverted_index.set,	110
abstract_inverted_index.set.	107
abstract_inverted_index.task	699, 748
abstract_inverted_index.than	340
abstract_inverted_index.that	243, 300, 323, 330, 362, 374, 391, 401, 612, 716
abstract_inverted_index.them	84, 229, 730
abstract_inverted_index.they	125, 244, 402, 677
abstract_inverted_index.this	63, 109, 165, 201, 535, 572
abstract_inverted_index.time	339
abstract_inverted_index.used	83, 290
abstract_inverted_index.user	375
abstract_inverted_index.well	515
abstract_inverted_index.were	102, 203, 467, 470
abstract_inverted_index.with	8, 16, 57, 130, 485, 586, 591, 671
abstract_inverted_index.work	192, 598
abstract_inverted_index.(ne*)	477
abstract_inverted_index.2.0.0	1039
abstract_inverted_index.2.0.1	1030
abstract_inverted_index.2.0.2	1020
abstract_inverted_index.2023,	614
abstract_inverted_index.2023.	704
abstract_inverted_index.2024,	596
abstract_inverted_index.2024.	629, 753
abstract_inverted_index.Agree	281, 296
abstract_inverted_index.Coded	578
abstract_inverted_index.Cysts	858
abstract_inverted_index.DICOM	540, 555, 564
abstract_inverted_index.Liver	877, 891, 908
abstract_inverted_index.Lungs	976
abstract_inverted_index.Major	357
abstract_inverted_index.Minor	298, 321, 326
abstract_inverted_index.NSCLC	934, 992
abstract_inverted_index.RIDER	936, 994
abstract_inverted_index.ROIs?	775
abstract_inverted_index.Score	267
abstract_inverted_index.about	506, 771, 787
abstract_inverted_index.added	210, 1032, 1041
abstract_inverted_index.agree	317
abstract_inverted_index.based	70, 451
abstract_inverted_index.brain	805
abstract_inverted_index.could	288
abstract_inverted_index.cover	729
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abstract_inverted_index.data.	21, 60
abstract_inverted_index.edits	299, 322, 327
abstract_inverted_index.ended	777, 795
abstract_inverted_index.file,	489, 493
abstract_inverted_index.file.	683
abstract_inverted_index.files	442
abstract_inverted_index.first	206
abstract_inverted_index.goal,	64
abstract_inverted_index.grade	273
abstract_inverted_index.label	737
abstract_inverted_index.model	176, 432, 815, 834, 849, 871, 885, 902, 917, 942, 945, 951, 970, 1000, 1016
abstract_inverted_index.often	14
abstract_inverted_index.other	17
abstract_inverted_index.rated	228
abstract_inverted_index.rates	707, 756
abstract_inverted_index.scan.	543
abstract_inverted_index.scans	828, 863, 880, 896, 912, 929, 961, 986, 1010
abstract_inverted_index.split	600
abstract_inverted_index.start	379, 585, 590
abstract_inverted_index.study	792
abstract_inverted_index.task.	420
abstract_inverted_index.tasks	78, 715
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abstract_inverted_index.there	723
abstract_inverted_index.these	38
abstract_inverted_index.those	329
abstract_inverted_index.tumor	806, 844
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abstract_inverted_index.would	376
abstract_inverted_index.~10%,	462
abstract_inverted_index.(i.e.,	284
abstract_inverted_index.(rad*)	474
abstract_inverted_index.Column	525
abstract_inverted_index.Folder	407
abstract_inverted_index.Likert	122, 233, 266, 518, 712, 764
abstract_inverted_index.Links:	814, 833, 848, 870, 884, 901, 916, 941, 969, 999, 1015
abstract_inverted_index.Manual	149
abstract_inverted_index.PET-CT	938, 996
abstract_inverted_index.PET/CT	827, 985
abstract_inverted_index.Scale,	234
abstract_inverted_index.Tumor,	856
abstract_inverted_index.agree'	129, 248
abstract_inverted_index.agreed	670
abstract_inverted_index.breast	824
abstract_inverted_index.called	545
abstract_inverted_index.cancer	11, 20
abstract_inverted_index.column	691, 740
abstract_inverted_index.common	422
abstract_inverted_index.create	37
abstract_inverted_index.edema,	809
abstract_inverted_index.edits.	358
abstract_inverted_index.effect	350
abstract_inverted_index.effort	34
abstract_inverted_index.ensure	187, 369
abstract_inverted_index.expert	170
abstract_inverted_index.files,	450
abstract_inverted_index.folder	423
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abstract_inverted_index.github	817, 836, 851, 873, 887, 904, 919, 948, 972, 1002, 1018
abstract_inverted_index.graded	114
abstract_inverted_index.image,	513
abstract_inverted_index.images	48, 701, 750
abstract_inverted_index.judges	333
abstract_inverted_index.labels	719
abstract_inverted_index.linked	15
abstract_inverted_index.manual	447
abstract_inverted_index.models	71
abstract_inverted_index.nnUNet	68
abstract_inverted_index.prefer	377
abstract_inverted_index.public	80
abstract_inverted_index.rating	727
abstract_inverted_index.record	140, 202
abstract_inverted_index.review	332
abstract_inverted_index.scale.	123, 713, 765
abstract_inverted_index.scans?	793
abstract_inverted_index.scores	150, 1052
abstract_inverted_index.stage.	207
abstract_inverted_index.within	182
abstract_inverted_index.years.	603
abstract_inverted_index.5-point	232
abstract_inverted_index.Breast,	840
abstract_inverted_index.Columns	500
abstract_inverted_index.Commons	3
abstract_inverted_index.Imaging	1
abstract_inverted_index.Kidney,	855
abstract_inverted_index.Lesions	893
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abstract_inverted_index.Nodules	925, 957
abstract_inverted_index.Segment	803, 819, 838, 853, 875, 889, 906, 921, 953, 974, 1004
abstract_inverted_index.Version	215, 262
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abstract_inverted_index.augment	53
abstract_inverted_index.between	608
abstract_inverted_index.change)	294
abstract_inverted_index.current	311
abstract_inverted_index.dataset	166
abstract_inverted_index.expense	32
abstract_inverted_index.folder.	647, 665
abstract_inverted_index.format.	458, 640, 658
abstract_inverted_index.further	62
abstract_inverted_index.images.	156
abstract_inverted_index.imaging	12
abstract_inverted_index.lesions	822, 979
abstract_inverted_index.limited	27
abstract_inverted_index.linking	481
abstract_inverted_index.medical	219
abstract_inverted_index.minimal	349
abstract_inverted_index.model's	94
abstract_inverted_index.outcome	353
abstract_inverted_index.output,	133, 621, 722
abstract_inverted_index.provide	49, 680
abstract_inverted_index.quality	116, 275, 393, 709, 761
abstract_inverted_index.ratings	495, 519
abstract_inverted_index.related	510
abstract_inverted_index.roughly	96
abstract_inverted_index.scratch	343, 382
abstract_inverted_index.segment	696, 745, 758
abstract_inverted_index.several	67
abstract_inverted_index.stages.	197
abstract_inverted_index.student	220
abstract_inverted_index.tissue,	842
abstract_inverted_index.trained	66
abstract_inverted_index.variety	74
abstract_inverted_index.version	952
abstract_inverted_index.weights	943, 946
abstract_inverted_index.without	293
abstract_inverted_index.Disagree	355
abstract_inverted_index.FDG-avid	821, 978
abstract_inverted_index.Improved	950
abstract_inverted_index.Manually	145
abstract_inverted_index.Overview	801
abstract_inverted_index.Prostate	1006
abstract_inverted_index.Reviewer	577
abstract_inverted_index.Strongly	280, 384
abstract_inverted_index.Versions	198, 208
abstract_inverted_index.analysis	45
abstract_inverted_index.analyzed	439
abstract_inverted_index.assigned	103
abstract_inverted_index.brain-mr	1024
abstract_inverted_index.category	360, 389
abstract_inverted_index.comments	521, 770, 786, 1036
abstract_inverted_index.connects	6
abstract_inverted_index.contains	429, 446, 504
abstract_inverted_index.contrast	860
abstract_inverted_index.datasets	81
abstract_inverted_index.details,	174
abstract_inverted_index.disagree	319, 385
abstract_inverted_index.enhanced	861
abstract_inverted_index.existing	54
abstract_inverted_index.expected	346
abstract_inverted_index.filename	632, 650
abstract_inverted_index.findings	789
abstract_inverted_index.generate	86
abstract_inverted_index.manually	575
abstract_inverted_index.metadata	505, 541, 556, 565
abstract_inverted_index.multiple	718
abstract_inverted_index.provided	163, 181, 252
abstract_inverted_index.provides	141
abstract_inverted_index.publicly	9
abstract_inverted_index.question	778, 796
abstract_inverted_index.records.	212, 265
abstract_inverted_index.regions:	807
abstract_inverted_index.required	35, 367
abstract_inverted_index.reviewed	222, 576, 618
abstract_inverted_index.reviewer	135, 668, 706, 755, 781, 799, 1051
abstract_inverted_index.specific	418
abstract_inverted_index.starting	341
abstract_inverted_index.strongly	669
abstract_inverted_index.training	177
abstract_inverted_index.utilized	259, 627
abstract_inverted_index.validate	92
abstract_inverted_index.verified	168
abstract_inverted_index.weights,	816, 835, 850, 872, 886, 903, 918, 971, 1001, 1017
abstract_inverted_index.‘ne’	592
abstract_inverted_index.'strongly	128, 247
abstract_inverted_index.Challenge	968
abstract_inverted_index.Changelog	1019
abstract_inverted_index.DICOM-SEG	435, 457
abstract_inverted_index.DICOM-seg	639, 657
abstract_inverted_index.PatientID	537, 538
abstract_inverted_index.SPIE-AAPM	965
abstract_inverted_index.Structure	408
abstract_inverted_index.Stylistic	304
abstract_inverted_index.TCGA-LIHC	883, 915
abstract_inverted_index.TCGA-LUAD	939, 997
abstract_inverted_index.TCGA-LUSC	940, 998
abstract_inverted_index.UPENN-GBM	813
abstract_inverted_index.Unusable.	387
abstract_inverted_index.Use-as-is	283
abstract_inverted_index.accurate?	689, 738
abstract_inverted_index.automatic	396
abstract_inverted_index.available	10
abstract_inverted_index.corrected	136, 146, 253, 448, 491
abstract_inverted_index.correctly	1029
abstract_inverted_index.directory	428, 445
abstract_inverted_index.enhancing	810
abstract_inverted_index.fraction,	461
abstract_inverted_index.increased	41
abstract_inverted_index.indicates	361, 390
abstract_inverted_index.inspected	153
abstract_inverted_index.manually.	39
abstract_inverted_index.necessary	364
abstract_inverted_index.necrosis,	808
abstract_inverted_index.performed	471
abstract_inverted_index.predicted	687, 735, 774
abstract_inverted_index.radiology	47, 76
abstract_inverted_index.reviewer.	582
abstract_inverted_index.reviewers	271
abstract_inverted_index.segments,	676
abstract_inverted_index.structure	424
abstract_inverted_index.treatment	292, 352
abstract_inverted_index.unusable.	404
abstract_inverted_index.‘rad’	587
abstract_inverted_index.(3mm-30mm)	926, 958
abstract_inverted_index.AI-derived	161
abstract_inverted_index.Collection	155, 527
abstract_inverted_index.Guidelines	269
abstract_inverted_index.ProstateX,	1013
abstract_inverted_index.QIN-Breast	829, 832
abstract_inverted_index.TCGA-KICH,	868
abstract_inverted_index.TCGA-KIRC,	866
abstract_inverted_index.TCGA-KIRP,	867
abstract_inverted_index.Validation	569
abstract_inverted_index.acceptable	314
abstract_inverted_index.additional	211, 264
abstract_inverted_index.annotation	59
abstract_inverted_index.associated	184
abstract_inverted_index.clinically	285, 308
abstract_inverted_index.collection	414, 441, 533
abstract_inverted_index.corrected.	468
abstract_inverted_index.correction	682
abstract_inverted_index.correlates	415
abstract_inverted_index.difference	607
abstract_inverted_index.important.	309
abstract_inverted_index.manuscript	185
abstract_inverted_index.necessary.	303, 325
abstract_inverted_index.non-expect	588
abstract_inverted_index.non-expert	251, 256, 616, 624
abstract_inverted_index.prediction	636, 654
abstract_inverted_index.reviewers.	524
abstract_inverted_index.structural	843
abstract_inverted_index.true/false	570
abstract_inverted_index.validation	106, 241
abstract_inverted_index.CPTAC-CCRCC	869
abstract_inverted_index.Collection:	812, 831, 846, 865, 882, 898, 914, 1012
abstract_inverted_index.Corrections	469
abstract_inverted_index.Definition:	268
abstract_inverted_index.Description	526
abstract_inverted_index.Radiologist	583
abstract_inverted_index.acceptable,	286
abstract_inverted_index.annotations	28, 162, 179, 397
abstract_inverted_index.collections	25, 56
abstract_inverted_index.lung-ct.zip	920
abstract_inverted_index.methodology	606
abstract_inverted_index.non-experts	476
abstract_inverted_index.opportunity	51
abstract_inverted_index.prediction,	455
abstract_inverted_index.predictions	101, 119, 226, 238, 433, 466
abstract_inverted_index.radiologist	113, 473, 490, 494, 1035
abstract_inverted_index.researchers	7
abstract_inverted_index.significant	373
abstract_inverted_index.transformed	1028
abstract_inverted_index.(non-expert)	221
abstract_inverted_index.Collections:	931, 963, 988
abstract_inverted_index.Description:	804, 820, 839, 854, 876, 890, 907, 922, 954, 975, 1005
abstract_inverted_index.QIN-LUNG-CT,	964
abstract_inverted_index.brain-mr.zip	802
abstract_inverted_index.capabilities	42
abstract_inverted_index.collections,	217
abstract_inverted_index.collections.	90
abstract_inverted_index.correctness,	370
abstract_inverted_index.differences,	305
abstract_inverted_index.liver-ct.zip	874
abstract_inverted_index.liver-mr.zip	905
abstract_inverted_index.lung2-ct.zip	949
abstract_inverted_index.performance,	95
abstract_inverted_index.performance.	498
abstract_inverted_index.radiologists	171
abstract_inverted_index.segmentation	77, 312, 419, 449, 488, 492, 708
abstract_inverted_index.study/series	483
abstract_inverted_index.sufficiently	372
abstract_inverted_index.transparency	188
abstract_inverted_index.HCC-TACE-SEG,	899
abstract_inverted_index.Radiogenomics	935, 993
abstract_inverted_index.breast-mr.zip	837
abstract_inverted_index.kidney-ct.zip	852
abstract_inverted_index.liver2-ct.zip	888
abstract_inverted_index.segmentation.	138
abstract_inverted_index.segmentations	87, 1025, 1044, 1049
abstract_inverted_index.AISegmentation	630
abstract_inverted_index.Anti-PD-1-Lung	932, 990
abstract_inverted_index.Fibroglandular	841
abstract_inverted_index.LUNG-PET-CT-Dx	933, 991
abstract_inverted_index.qa-results.csv	478, 499, 502, 1038
abstract_inverted_index.segmentations,	144, 147, 508
abstract_inverted_index.segmentations.	254, 278
abstract_inverted_index.{SEGMENT_NAME}	736
abstract_inverted_index.AimiProjectYear	593, 703, 752
abstract_inverted_index.board-certified	112
abstract_inverted_index.prostate-mr.zip	1003
abstract_inverted_index.StudyInstanceUID	551, 552
abstract_inverted_index.reproducibility.	190
abstract_inverted_index.SeriesInstanceUID	560, 561
abstract_inverted_index.reviewer-corrected	653
abstract_inverted_index.ACRIN-NSCLC-FDG-PET	989
abstract_inverted_index.lung-fdg-pet-ct.zip	973
abstract_inverted_index.ai-segmentations-dcm	426, 646
abstract_inverted_index.qa-segmentations-dcm	443, 664
abstract_inverted_index.CorrectedSegmentation	648
abstract_inverted_index.breast-fdg-pet-ct.zip	818
abstract_inverted_index.Prostate-MRI-US-Biopsy	1014
abstract_inverted_index.duke-breast-cancer-mri	847
abstract_inverted_index.COLORECTAL-LIVER-METASTASES	900
abstract_inverted_index.(IDC)(https://imaging.datacommons.cancer.gov/)	4
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	103
sustainable_development_goals[0].id	https://metadata.un.org/sdg/17
sustainable_development_goals[0].score	0.5699999928474426
sustainable_development_goals[0].display_name	Partnerships for the goals
citation_normalized_percentile