[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120156-en":3,"doc-seo-120156-105":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},120156,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Framework for Interpretability in Machine Learning for Medical Imaging - Step-by-Step Guide","Interpretability for machine learning models in medical imaging (MLMI) addresses an important need, yet the field often lacks clear agreement on what interpretability means and why it is required. This work formalizes interpretability goals and elements by reasoning over real-world tasks shared between medical image analysis and machine learning. It identifies five core elements—localization, visual recognizability, physical attribution, model transparency, and actionability—then provides a step-by-step framework to guide method design and evaluation for practical deployment.","16 Apr 2024  \nDate of publication April 09, 2024, date of current version April 09, 2024.  \nDigital Object Identifier 10.1109/ACCESS.2024.3387702  \nA Framework for Interpretability  \nin Machine Learning for Medical Imaging  \nALAN Q. WANG 1,2, BATUHAN K. KARAMAN 1,2, HEEJONG KIM2, JACOB ROSENTHAL2,3, RACHITSALUJA 1,2, SEAN I. YOUNG4,5, and MERT R. SABUNCU 1,2  \n1 School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY 10044, USA 2Department of Radiology, Weill Cornell Medical School, New York, NY 10065, USA  \n3Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY 10065, USA 4Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129, USA 5Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA Corresponding author: Alan Q. Wang (email: [aw847@cornell.edu](aw847@cornell.edu)).  \nFunding for this project was in part provided by the NIH grants R01AG053949 and T32GM007739, and the NSF CAREER 1748377 grant.  \n ABSTRACT Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why  \n[ cs .LG]  \ndoes the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching  \narXiv :23 10 .01685v3  \ninterpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research.  \n INDEX TERMS Interpretability, explainability, medical imaging, machine learning.  \nI. INTRODUCTION  \nMachine learning (ML) has seen remarkable advancement in recent years. ML’s intersection with medical imaging (which we abbreviate as MLMI) is amongst the most promising, offering potential advances to quality of patient care [1–3] . However, the most performant machine learning models like those in computer vision and deep learning are generally regarded as black boxes – they output predictions without revealing to human users how they arrived at those predictions. As such, there has been a surge of papers calling for and proposing methods that make their decision-making interrogable, understandable, or explainable by users. This subfield has gone by names like ‘‘interpretable,\" ‘‘explainable,\" and‘‘transparent\" ML. There is a clear need for such methods [4, 5], and the rising interest in this field is a reflection of the safety-critical, high-stakes setting in which medical imaging applications are deployed.  \nHowever, in the medical imaging field, there is a gen-  \neral sense of murkiness in how these words are or can be used. Many works claim a certain approach to be ‘‘noninterpretable’’ and thus sub-optimal, while others claim to increase interpretability, without a formalism that we can commonly agree on. Our motivation in this paper is to introduce a formal framework to use for considering, motivating, studying, validating, and discussing interpretability in MLMI. Central to our work is answ","cbCaiusB8pJobokm","https://ap.wps.com/l/cbCaiusB8pJobokm","pdf",5764015,1,19,"English","en",105,"# Introduction\n## Problem: murkiness in interpretability terms\n## Five core elements of interpretability in MLMI\n## Proposed framework and step-by-step guidance","[{\"question\":\"Why does interpretability matter for medical imaging machine learning models?\",\"answer\":\"Interpretability is needed because medical imaging deployments are safety-critical and high-stakes, yet powerful models are often treated as black boxes without revealing how predictions are formed.\"},{\"question\":\"What five core elements define interpretability in MLMI?\",\"answer\":\"The framework identifies localization, visual recognizability, physical attribution, model transparency, and actionability as the five core elements.\"},{\"question\":\"How does the paper help practitioners approach interpretability in MLMI?\",\"answer\":\"It formalizes interpretability needs for medical imaging and offers a step-by-step guide to clarify MLMI-specific goals and considerations when designing and evaluating methods.\"}]","A Framework for Interpretability in Machine Learning for Medical Imaging - 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