[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121612-en":3,"doc-seo-121612-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121612,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","AAPM Task Group Report 273 - Recommendations on Best Practices for AI and Machine Learning for Computer-Aided Diagnosis in Medical Imaging","Peer-reviewed recommendations for applying artificial intelligence and machine learning to computer-aided diagnosis in medical imaging. The report organizes guidance across data sourcing and sampling, including ethics, de-identification, diversity and inclusion, quality, augmentation, and harmonization. It further addresses reference standards and annotation practices, comparing objective versus subjective targets and multiple annotation granularities. It concludes with practical model development considerations, covering sampling strategies and learning paradigms, from supervised to self-supervised approaches.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nAAPM Task Group Report 273: Recommendations on Best Practices for AI and Machine Learning for Computer-Aided Diagnosis in Medical Imaging  \nOriginal  \nAAPM Task Group Report 273: Recommendations on Best Practices for AI and Machine Learning for Computer-Aided Diagnosis in Medical Imaging / Hadjiiski, Lubomir; Cha, Kenny; Chan, Heang-Ping; Drukker, Karen; Morra, Lia; Näppi, Janne J. ; Sahiner, Berkman; Yoshida, Hiroyuki; Chen, Quan; Deserno, Thomas M. ; Greenspan, Hayit; Huisman, Henkjan; Huo, Zhimin; Mazurchuk, Richard; Petrick, Nicholas; Regge, Daniele; Samala, Ravi; Summers, Ronald M. ; Suzuki, Kenji; Tourassi, Georgia; Vergara, Daniel; Armato III, Samuel G.. -In: MEDICAL PHYSICS. -ISSN 0094-2405. - :2ai2aiiy):, pp. 1-24. [10.1002/mp.16188]  \nThis version is available at: 11583/2973414 since: 2023-01-06T15:19:18Z  \nPublisher:  \nWiley  \nPublished  \nDOI:10.1002/mp.16188  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nWiley postprint/Author's Accepted Manuscript  \nThis is the peer reviewed version of the above quoted article, which has been published in final form at [http://dx.doi.org/10.1002/mp.16188.This article may be](http://dx.doi.org/10.1002/mp.16188.This article may be) used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.  \n(Article begins on next page)  \n18 September 2024  \n1 AAPM Task Group Report 273: Recommendations on Best  \n2 Practices for AI and Machine Learning for Computer-Aided  \n3 Diagnosis in Medical Imaging  \n4  \n5 Lubomir Hadjiiski  \n6 Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA 7  \n8 Kenny Cha  \n9 U.S. Food and Drug Administration, Silver Spring, Maryland, USA 10  \n11 Heang-Ping Chan  \n12 Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA 13  \n14 Karen Drukker  \n15 Department of Radiology, University of Chicago, Chicago, Illinois, USA 16  \n17 Lia Morra  \n18 Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy 19  \n20 Janne J. Näppi  \n21 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard  \n22 Medical School, Boston, Massachusetts, USA 23  \n24 Berkman Sahiner  \n25 U.S. Food and Drug Administration, Silver Spring, Maryland, USA 26  \n27 Hiroyuki Yoshida  \n28 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard  \n29 Medical School, Boston, Massachusetts, USA 30  \n31 Quan Chen  \n32 Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA 33  \n34 Thomas M. Deserno  \n35 Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical  \n36 School, Braunschweig, Germany 37  \n38 Hayit Greenspan  \n39 Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, 40 Israel & Department of Radiology, Ichan School of Medicine, Mt Sinai, NYC, NY, USA  \n41  \n42  \n43 Henkjan Huisman  \n44 Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The  \n45 Netherlands 46  \n47 Zhimin Huo  \n48 Tencent America, Palo Alto, CA 49  \n50 Richard Mazurchuk  \n51 Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, 52 Bethesda, Maryland, USA  \n53  \n54 Nicholas Petrick  \n55 U.S. Food and Drug Administration, Silver Spring, Maryland, USA 56  \n57 Daniele Regge  \n58 Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo  \n59 Department of Surgical Sciences, University of Turin, Turin, Italy 60  \n61 Ravi Samala  \n62 U.S. Food and Drug Administration, Silver Spring, Maryland, USA 63  \n64 Ronald M. Summers  \n65 Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, 66 Maryland, USA  \n67  \n68 Kenji Suzuki  \n69 Institute of Innovative Research, Tokyo Institute of Tech","cbCairPT6G5uoywR","https://ap.wps.com/l/cbCairPT6G5uoywR","pdf",500967,1,45,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Data\n## 2.1 Data Collection\n## 2.2 Data Augmentation\n## 2.3 Data Harmonization\n## 2.4 Take Home Message on Data\n# 3 Reference Standards\n## 3.1 Objective vs. Subjective Reference Standards\n## 3.2 Annotation Granularity\n## 3.3 Methods for Acquiring Annotations\n## 3.5 Take Home Message on Reference Standards\n# 4 Model Development\n## 4.1 Data Sampling Strategies\n## 4.2 Machine Learning Strategies","[{\"question\":\"What topics are covered under the report’s guidance for data?\",\"answer\":\"The report covers data collection and case sampling, public databases, ethics considerations (including de-identification), diversity and inclusion, quality considerations, plus data augmentation and data harmonization.\"},{\"question\":\"How does the report address reference standards and annotations?\",\"answer\":\"It discusses objective versus subjective reference standards, multiple annotation granularities (entire image, region-based, pixel-based), methods for acquiring annotations (expert labels, electronic health record, crowd sourcing, phantoms, weak/noisy labels), and how true positives are defined.\"},{\"question\":\"Which machine learning approaches are included in model development recommendations?\",\"answer\":\"Model development guidance includes data sampling strategies and learning supervision levels, spanning supervised, semi-supervised, self-supervised, and unsupervised learning.\"}]","AAPM Task Group Report 273 - 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