No Masks Needed: Explainable AI for Deriving Segmentation from Classification
Dept. Electrical and Electronic Engineering, Imperial College London（帝国理工学院伦敦分校电子与电气工程系）；Centre for Vision, Speech and Signal Processing, University of Surrey（萨里大学视觉、语音与信号处理中心）
Medical image segmentation is vital for modern healthcare and is a key element of computer-aided diagnosis. While recent advancements in computer vision have explored unsupervised segmentation using pre-trained models, these methods have not been translated well to the medical imaging domain. In this work, we introduce a novel approach that fine-tunes pre-trained models specifically for medical images, achieving accurate segmentation with extensive processing. Our method integrates Explainable AI to generate relevance scores, enhancing the segmentation process. Unlike traditional methods that excel in standard benchmarks but falter in medical applications, our approach achieves improved results on datasets like CBIS-DDSM, NuInsSeg and Kvasir-SEG.
Explainable Continuous-Time Mask Refinement with Local Self-Similarity Priors for Medical Image Segmentation
School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India（计算机工程学院，印度克里希纳加尔工业技术学院，布巴内斯瓦尔，印度）；AmygdalaAI-India Lab, Bhubaneswar, India（AmygdalaAI-印度实验室，布巴内斯瓦尔，印度）
Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, HHI（弗劳恩霍夫电信研究所，海因里希·赫兹研究所，HHI）；Humboldt University Berlin（柏林洪堡大学）
The new era of image segmentation leveraging the power of Deep Neural Nets (DNNs) comes with a price tag: to train a neural network for pixel-wise segmentation, a large amount of training samples has to be manually labeled on pixel-precision. In this work, we address this by following an indirect solution. We build upon the advances of the Explainable AI (XAI) community and extract a pixel-wise binary segmentation from the output of the Layer-wise Relevance Propagation (LRP) explaining the decision of a classification network. We show that we achieve similar results compared to an established …
GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学杭州国际科创中心）
IIT Kharagpur（印度理工学院卡拉格普尔分校）；University of Poitiers（普瓦捷大学）；IIT Jodhpur（印度理工学院焦特布尔分校）
Deep neural networks have shown their profound impact on achieving human level performance in visual saliency prediction. However, it is still unclear how they learn the task and what it means in terms of understanding human visual system. In this work, we develop a technique to derive explainable saliency models from their corresponding deep neural architecture based saliency models by applying human perception theories and the conventional concepts of saliency. This technique helps us understand the learning pattern of the deep network at its intermediate layers through their activation maps…
Manual labeling of gestures in robot-assisted surgery is labor intensive, prone to errors, and requires expertise or training. We propose a method for automated and explainable generation of gesture transcripts that leverages the abundance of data for image segmentation. Surgical context is detected using segmentation masks by examining the distances and intersections between the tools and objects. Next, context labels are translated into gesture transcripts using knowledge-based Finite State Machine (FSM) and data-driven Long Short Term Memory (LSTM) models. We evaluate the performance of eac…
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
TECNALIA（泰克纳利亚技术中心）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟）；University of Deusto（德乌斯托大学）；University of the Basque Country (UPV/EHU)（巴斯克大学）
Understanding model predictions is critical in healthcare, to facilitate rapid verification of model correctness and to guard against use of models that exploit confounding variables. We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images, in which a model must indicate the regions used to predict each abnormality. To solve this task, we propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. Next we incorporate HiResCAM, an attention mechanism, to …
The Class Activation Map (CAM) lookup of a neural network tells us to which regions the neural network focuses when it makes a decision. In the past, the CAM search method was dependent upon a specific internal module of the network. It has specific constraints on the structure of the neural network. To make the search of CAM have generality and high performance. We propose a learning-based algorithm, namely Multiple Dynamic Masks (MDM). It is based on a public cognition that only active features of a picture related to classification will affect the classification results of the neural networ…
Training-free Test-time Improvement for Explainable Medical Image Classification
Department of Biomedical Engineering, Peking University, Beijing, China（北京大学生物医学工程系）；Institute of Medical Technology, Peking University, Beijing, China（北京大学医学技术研究所）；National Biomedical Imaging Center, Peking University, Beijing, China（北京大学国家生物医学成像中心）；Beijing Institute of Technology, Beijing, China（北京理工大学）；Equal Contribution（同等贡献）；Corresponding Author（通讯作者）
Counterfactuals and Uncertainty-Based Explainable Paradigm for the Automated Detection and Segmentation of Renal Cysts in Computed Tomography Images: A Multi-Center Study
Maastricht University（马斯特里赫特大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Maastricht University Medical Center+（马斯特里赫特大学医学中心+）；University of California-San Francisco（加州大学旧金山分校）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；GROW Research Institute for Oncology and Reproduction（GROW肿瘤学与生殖学研究所）
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricu…
Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection
Nanyang Technological University（南洋理工大学）；Ant Digital Technologies, Ant Group（蚂蚁数字科技，蚂蚁集团）；Singapore University of Technology and Design（新加坡科技设计大学）；Singapore Management University（新加坡管理大学）
From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring
Intelligent Maintenance and Operations Systems (IMOS), EPFL（智能维护与运营系统研究所（IMOS），洛桑联邦理工学院）；Environmental Computational Science and Earth Observation (ECEO), EPFL（环境计算科学与地球观测研究所（ECEO），洛桑联邦理工学院）
Bridging visual saliency and large language models for explainable deep learning in medical imaging
Department of Mathematics and Computer science（数学与计算机科学系）；Institute of Cognitive Science（认知科学研究所）；Rhodes University（罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（理论与计算科学国家研究所）
FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东省超高清沉浸式媒体技术重点实验室）；School of Future Technology, South China University of Technology（华南理工大学未来技术学院）
Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset
Faculty of Computer Engineering, K. N. Toosi University of Technology（计算机工程学院，K.N.托菲大学）；Faculty of Electrical Engineering, K. N. Toosi University of Technology（电气工程学院，K.N.托菲大学）；Faculty of Mechanical Engineering, Tarbiat Modares University（机械工程学院，塔里比亚特莫达res大学）
Light field (LF) camera captures rich information from a scene. Using the information, the LF de-occlusion (LF-DeOcc) task aims to reconstruct the occlusion-free center view image. Existing LF-DeOcc studies mainly focus on the sparsely sampled (sparse) LF images where most of the occluded regions are visible in other views due to the large disparity. In this paper, we expand LF-DeOcc in more challenging datasets, densely sampled (dense) LF images, which are taken by a micro-lens-based portable LF camera. Due to the small disparity ranges of dense LF images, most of the background regions are i…
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University（复旦大学附属中山医院内镜中心及内镜研究所）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria（维也纳医科大学医学数据科学中心人工智能研究所）；Christian Doppler Lab for Artificial Intelligence in Retina, Medical University of Vienna, Austria（维也纳医科大学视网膜人工智能克里斯蒂安·多普勒实验室）
International Institute of Information Technology Bangalore（国际信息技术学院班加罗尔）
Deep neural networks have become the default choice for many applications like image and video recognition, segmentation and other image and video related tasks.However, a critical challenge with these models is the lack of explainability.This requirement of generating explainable predictions has motivated the research community to perform various analysis on trained models.In this study, we analyze the learned feature maps of trained models using MNIST images for achieving more explainable predictions.Our study is focused on deriving a set of primitive elements, here called visual concepts, t…
PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation
Department of Computer Science, Quaid-e-Awam University of Engineering, Sciences & Technology（计算机科学系，卡迪尔-阿瓦姆工程、科学与技术大学）；Department of Artificial Intelligence, Quaid-e-Awam University of Engineering, Sciences & Technology（人工智能系，卡迪尔-阿瓦姆工程、科学与技术大学）；Department of Computer Science, Sindh Madressatul Islam University, City Campus, Karachi（计算机科学系， Sind 阿里斯坦伊斯兰大学，卡拉奇城校区）；Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London（计算机科学与数字技术系，建筑、计算与工程学院，东伦敦大学）
retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers
Department of Ophthalmology, Erasmus University Medical Center（埃因霍温大学医学中心眼科系）；Department of Epidemiology, Erasmus University Medical Center（埃因霍温大学医学中心流行病学系）；Department of Ophthalmology, Radboud University Medical Center（拉德堡德大学医学中心眼科系）；Institute of Molecular and Clinical Ophthalmology, University of Basel（巴塞尔大学分子与临床眼科研究所）；Dept. of Computational Biology, University of Lausanne（洛桑大学计算生物学系）；Swiss Institute of Bioinformatics, Lausanne, Switzerland（瑞士生物信息学研究所，洛桑，瑞士）；Dept. of Integrative Biomedical Sciences, University of Cape Town（开普敦大学整合生物医学科学系）
Mila, Québec Artificial Intelligence Institute（米拉魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Imagia Cybernetics；CIFAR（加拿大高等研究院）
Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take …
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Columbia University（哥伦比亚大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of Maryland, College Park（马里兰大学帕克分校）；University of California, Irvine（加州大学欧文分校）；University of Rochester（罗切斯特大学）；Universidade Federal de São Paulo（圣保罗联邦大学）；Duke University（杜克大学）；Emory University（埃默里大学）
