Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentation, and other tasks. However, they come with the inherent drawback of inexplicability limiting their applicability and trustworthiness. Although there exists work addressing this perspective, most of the existing approaches are limited to the image modality due to the intuitive and prominent concepts. Conversely, the concepts in the time-series domain are more complex and non-comprehensive but these and an explanation for the network decision are pivotal in critical domains like medical, financial, or industry. Addressing the need for an explainable approach, we propose a novel interpretable network scheme, designed to inherently use an explainable reasoning process inspired by the human cognition without the need of additional post-hoc explainability methods. Therefore, class-specific patches are used as they cover local concepts relevant to the classification to reveal similarities with samples of the same class. In addition, we introduce a novel loss concerning interpretability and accuracy that constraints P2ExNet to provide viable explanations of the data including relevant patches, their position, class similarities, and comparison methods without compromising accuracy. Analysis of the results on eight publicly available time-series datasets reveals that P2ExNet reaches comparable performance when compared to its counterparts while inherently providing understandable and traceable decisions.
Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）
PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis
Christian Doppler Laboratory for Artificial Intelligence in Retina（人工智能在视网膜领域的克里斯蒂安·多普勒实验室）；Institute of Artificial Intelligence（人工智能研究所）；Center for Medical Data Science（医学数据科学中心）；Medical University of Vienna（维也纳医学大学）
From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification
Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research（杰哥利昂人中心人工智能实验室、马克·卡复杂系统研究中心）；Jagiellonian University（杰哥利昂大学）；Department of Computer Science（计算机科学系）；Norwegian University of Science and Technology (NTNU)（挪威科学技术大学（NTNU））；Department of Human-Centered Artificial Intelligence（人中心人工智能系）；Institute of Applied Computer Science（应用计算机科学研究所）；School of Information Technology（信息科技学院）
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
Yonsei University（延世大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）
MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features
School of Computing Sciences, University of East Anglia（东安格利亚大学计算机科学学院）；Norfolk and Norwich University Hospital（诺福克和诺里奇大学医院）；University of East Anglia（东安格利亚大学）
Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
Department of Biomedical Sciences, Humanitas University（生物医学科学系，人道大学）；Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital（放射治疗与放射外科系，IRCCS人道研究医院）
Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery
Faculty of Information Technology, University of Science, Vietnam National University（信息技术学院，科学大学，越南国家大学）；Department of Computer Science, University of Warwick（计算机科学系，沃里克大学）
Sparse Prototype Network for Explainable Pedestrian Behavior Prediction
Institutes of Innovation for Future Society, Nagoya University（名古屋大学未来社会创新研究院）；Graduate School of Engineering, Gifu University（岐阜大学工学研究科）；Tier IV Inc.（Tier IV公司）；Nagoya University Open Innovation Center（名古屋大学开放创新中心）；Graduate School of Informatics, Nagoya University（名古屋大学信息学研究科）
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development
University of Tennessee Health Science Center（田纳西大学健康科学中心）；Oak Ridge National Laboratory（橡树岭国家实验室）；College of Medicine（医学院）
