Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases. In this work, we synthesize foundational work on IML methods and evaluation into an actionable taxonomy. This taxonomy serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components. It also provides the foundation from which we describe a three-step workflow to better enable researchers and consumers to work together to discover what types of methods are useful for what use cases. Eventually, by building on the results generated from this workflow, a more complete version of the taxonomy will increasingly allow consumers to find relevant methods for their target use cases and researchers to identify applicable use cases for their proposed methods.
Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Digital Technology Research Center, National Research Council Canada（加拿大国家研究理事会数字技术研究中心）；Biomedical Science and Hematology Departments, College of Medicine and Health Sciences, Sultan Qaboos University（苏丹奥曼穆萨特大学医学院与健康科学学院生物医学科学与血液科）
Streaming Adversarial Robustness in Fuzzy ARTMAP: Mechanism-Aligned Evaluation, Progressive Training, and Interpretable Diagnostics
Department of Electrical and Computer Engineering（电气与计算机工程系）；Kummer Institute Center for Artificial Intelligence and Autonomous Systems (KICAIAS)（人工智能与自主系统中心（KICAIAS））；Missouri University of Science and Technology（密苏里科技大学）
Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical Interpolation
School of Mechanical Engineering, Purdue University（普渡大学机械工程学院）；Mathematics and Computer Science Division, Argonne National Laboratory（阿贡国家实验室数学与计算机科学部）
Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Technical University of Darmstadt（达姆施塔特工业大学）
LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype
Stanford University（斯坦福大学）；Stanford University School of Medicine（斯坦福大学医学院）；Harvard Medical School（哈佛医学院）；La Liga Nacional Contra el Cáncer de Guatemala (INCAN)（危地马拉国家抗癌联盟（INCAN））；Fred Hutchinson Cancer Research Center（弗雷德·哈钦森癌症研究中心）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
Technical University Munich（慕尼黑工业大学）；Ludwig Maximilians University（慕尼黑大学）
Alzheimer's disease (AD) has a complex and multifactorial etiology, which requires integrating information about neuroanatomy, genetics, and cerebrospinal fluid biomarkers for accurate diagnosis. Hence, recent deep learning approaches combined image and tabular information to improve diagnostic performance. However, the black-box nature of such neural networks is still a barrier for clinical applications, in which understanding the decision of a heterogeneous model is integral. We propose PANIC, a prototypical additive neural network for interpretable AD classification that integrates 3D image…
Explainable AI (XAI) is a necessity in safety-critical systems such as in clinical diagnostics due to a high risk for fatal decisions. Currently, however, XAI resembles a loose collection of methods rather than a well-defined process. In this work, we elaborate on conceptual similarities between the largest subgroup of XAI, interpretable machine learning (IML), and classical statistics. Based on these similarities, we present a formalization of IML along the lines of a statistical process. Adopting this statistical view allows us to interpret machine learning models and IML methods as sophisti…
Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
Department of Statistics and Data Science, Carnegie Mellon University（统计与数据科学系，卡内基梅隆大学）；Department of Statistics, Federal University of Sao Carlos（统计系，圣卡洛斯联邦大学）
Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers
Biomedical AI Research Lab, University of California, Los Angeles（加州大学洛杉矶分校生物医学人工智能研究实验室，加州大学洛杉矶分校）；Department of Radiology, University of California, Los Angeles（加州大学洛杉矶分校放射科）；Bioinformatics Interdepartmental Program, University of California, Los Angeles（加州大学洛杉矶分校生物信息学跨学科项目）；Department of Bioengineering, University of California, Los Angeles（加州大学洛杉矶分校生物工程系）；Department of Pathology & Laboratory Medicine, University of California, Los Angeles（加州大学洛杉矶分校病理学与实验室医学系）
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data
University of Pennsylvania（宾夕法尼亚大学）；University of Electronic Science and Technology（电子科技大学）；Beijing Tongren Eye Center（北京同仁眼科中心）；Beijing Tongren Hospital（北京同仁医院）；Capital Medical University（首都医科大学）
Multilayer Q-Matrix-Embedded Neural Network for Cognitive Diagnosis (M-QCDNet): Structure-Aware Deep Learning Architecture for Psychometric Interpretability
KTH Royal Institute of Technology（瑞典皇家理工学院）；Carnegie Mellon University（卡内基梅隆大学）；Karolinska Institute（卡罗林斯卡学院）
Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus on the task of diagnostic prediction using discomfort drawings, and explore two ways to employ causal identification to improve the diagnostic results. Firstly, we use causal identification to infer the causal relationships among diagnostic labels which, by itself, provides inte…
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods. However, their complexity makes the results hard to interpret without additional tools. There has been a lot of recent work in developing global and local diagnostics for interpreting SML models. In this paper, we propose a locally-interpretable model that takes the fitted ML response surface, partitions the predictor space using model-based regression trees, and fits i…
Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis
School of Electrical Engineering and Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；The Centre for Advanced Imaging, University of Queensland（昆士兰大学高级成像中心）；Australian Institute for Bioengineering and Nanotechnology, University of Queensland（昆士兰大学生物工程与纳米技术研究所）；ARC Training Centre for Innovation in Biomedical Imaging Technology（ARC生物医学成像技术创新培训中心）
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
Xi’an Jiaotong University（西安交通大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；UiT - Arctic University of Norway（挪威北极大学）；Fourth Military Medical University（空军军医大学）
Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data
University of Idaho, Department of Computer Science（爱达荷大学计算机科学系）；Jouf University, College of Computer and Information Sciences, Department of Computer Science（朱夫大学计算机与信息科学学院计算机科学系）；University of Gezira, Faculty of Mathematical and Computer Sciences（杰兹拉大学数学与计算机科学学院）；Bentonville High School（伯恩斯维尔高中）；University of KwaZulu-Natal, School of Mathematics, Statistics and Computer Science（夸祖鲁-纳塔尔大学数学、统计与计算机科学学院）
Capital Normal University（首都师范大学）；Beijing Hospital（北京医院）；National Center of Gerontology（国家老年医学中心）；Institute of Geriatric Medicine, Chinese Academy of Medical Sciences（中国医学科学院老年医学研究所）；Savaid Medical School, University of Chinese Academy of Sciences（中国科学院大学存济医学院）；Beijing University of Technology（北京工业大学）；University of Chinese Academy of Sciences（中国科学院大学）
Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from f…
