Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Shenzhen Polytechnic University（深圳职业技术大学）；University of Macau（澳门大学）；Southern University of Science and Technology（南方科技大学）
Limai Jiang, Ruitao Xie, Bokai Yang, Huazhen Huang, Juan He, Yufu Huo, Zikai Wang, Yang Wei, Yunpeng Cai
Medical image segmentation plays a vital role in clinical decision-making, enabling precise localization of lesions and guiding interventions. Despite significant advances in segmentation accuracy, the black-box nature of most deep models has raised growing concerns about their trustworthiness in high-stakes medical scenarios. Current explanation techniques have primarily focused on classification tasks, leaving the segmentation domain relatively underexplored. We introduced an explanation model for segmentation task which employs the causal inference framework and backpropagates the average treatment effect (ATE) into a quantification metric to determine the influence of input regions, as well as network components, on target segmentation areas. Through comparison with recent segmentation explainability techniques on two representative medical imaging datasets, we demonstrated that our approach provides more faithful explanations than existing approaches. Furthermore, we carried out a systematic causal analysis of multiple foundational segmentation models using our method, which reveals significant heterogeneity in perceptual strategies across different models, and even between different inputs for the same model. Suggesting the potential of our method to provide notable insights for optimizing segmentation models. Our code can be found at https://github.com/lcmmai/PdCR.
CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention
Wuhan University of Technology（武汉理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；MedAI Technology (Wuxi) Co. Ltd.（MedAI科技（无锡）有限公司）；Technical University of Munich（慕尼黑工业大学）
Multimodal Causal-Driven Representation Learning for Generalizable Medical Image Segmentation
City University of Hong Kong（香港城市大学）；Shenzhen Loop Area Institute（深圳河套学院）；CAIR, HKISI, Chinese Academy of Sciences（中国科学院计算智能研究所）；UESTC（电子科技大学）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network
Tianjin University（天津大学）；Tianjin Key Laboratory of Cognitive Computing and Application（天津认知计算与应用重点实验室）；Tianjin Key Laboratory of Advanced Networking（天津先进网络重点实验室）；Tianjin International Engineering Institute, Tianjin University（天津大学国际工程师学院）；Tianjin Central Hospital of Gynecology Obstetrics（天津市中心妇产科医院）；Silesian University of Technology（西里西亚工业大学）；University of Alberta（阿尔伯塔大学）；Istinye University（伊斯坦布尔耶尼大学）
Statistical machine learning algorithms have achieved state-of-the-art results on benchmark datasets, outperforming humans in many tasks. However, the out-of-distribution data and confounder, which have an unpredictable causal relationship, significantly degrade the performance of the existing models. Causal Representation Learning (CRL) has recently been a promising direction to address the causal relationship problem in vision understanding. This survey presents recent advances in CRL in vision. Firstly, we introduce the basic concept of causal inference. Secondly, we analyze the CRL theoret…
Structure Causal Models and LLMs Integration in Medical Visual Question Answering
School of Microelectronics, Tianjin University（天津大学微电子学院）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）
Causal Disentanglement for Robust Long-tail Medical Image Generation
School of Electrical and Information Engineering, Tianjin University, China（天津大学电气与信息工程学院）；Department of Information Engineering and Computer Science, University of Trento, Italy（特伦托大学信息工程与计算机科学系）；Fondazione Bruno Kessler, Italy（布鲁诺·克塞勒基金会）
How can we understand classification decisions made by deep neural networks? Many existing explainability methods rely solely on correlations and fail to account for confounding, which may result in potentially misleading explanations. To overcome this problem, we define the Causal Concept Effect (CaCE) as the causal effect of (the presence or absence of) a human-interpretable concept on a deep neural net's predictions. We show that the CaCE measure can avoid errors stemming from confounding. Estimating CaCE is difficult in situations where we cannot easily simulate the do-operator. To mitigat…
Deep learning methods usually require a large amount of training data and lack interpretability. In this paper, we propose a novel knowledge distillation and model interpretation framework for medical image classification that jointly solves the above two issues. Specifically, to address the data-hungry issue, a small student model is learned with less data by distilling knowledge from a cumbersome pretrained teacher model. To interpret the teacher model and assist the learning of the student, an explainer module is introduced to highlight the regions of an input that are important for the pre…
Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognition
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR)（联合山东大学-东北大学人工智能研究中心（C-FAIR））；Shandong University（山东大学）；School of Information Science and Engineering（信息科学与工程学院）；Child Care Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属儿童医院）；Case Western Reserve University（凯斯西储大学）
Imperial College London（帝国理工学院）；TU München（慕尼黑工业大学）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust…
UC San Diego（加州大学圣地亚哥分校）；Stanford University（斯坦福大学）
Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfactual explanations are very effective in providing transparency for these black-box algorithms. Nevertheless, generating counterfactuals that can have a consistent impact on classifier outputs and yet expose interpretable feature changes is a very challenging task. We introduce a novel method to generate causal and yet interpretable counterfactual explanations for image classifiers using pretrained generative models wi…
University of Oxford（牛津大学）；University College London（伦敦大学学院）；Thames Valley Deanery（泰晤士谷医学院）；TU Wien（维也纳工业大学）
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work, we introduce the task of generating natural language explanations (NLEs) to justify predictions made on medical images. NLEs are human-friendly and comprehensive, and enable the training of intrinsically explainable models. To this goal, we introduce MIMIC-NLE, the first, large-scale, medical imagi…
Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）；Deepwise AI Lab（深睿医疗AI实验室）
We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part; b) the disease (non-clinical)-related part; c) others, with only a),b) causally related to the disease however c) may contain spurious co…
This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice. Looking at these challenges through the lens of causality allows decisions about data collection, annotation procedures, and learning strategies to be made (and scrutinized) more transparently. We discuss how causal relationships between images and annotations can not only have profound…
Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
School of Microelectronics, Tianjin University（天津大学微电子学院）；Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University（江苏大学附属昆山医院骨科部）；Department of Clinical Laboratory, The Third Central Hospital of Tianjin（天津第三中心医院临床实验室）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）
The Boundaries of Fair AI in Medical Image Prognosis: A Causal Perspective
Department of Computer Science and Engineering, The Ohio State University（计算机科学与工程系，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University（生物医学信息学系，俄亥俄州立大学）；Department of Ophthalmology and Visual Sciences, The Ohio State University（眼科学与视觉科学系，俄亥俄州立大学）
Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major advantages for medical image classification over traditional methods, clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. In recent years, this has been addressed by a variety of approaches that have successfully contributed to providing deeper insight. Most notably, additive feature attribution methods are…
Causal Attribution of Model Performance Gaps in Medical Imaging Under Distribution Shifts
CIBM Center for Biomedical Imaging（生物医学成像中心）；Department of Radiology, Lausanne University Hospital and University of Lausanne（拉沃大学医院放射科）；MedGIFT, Institute of Informatics, School of Management, HES–SO Valais–Wallis University of Applied Sciences and Arts Western Switzerland（MedGIFT，信息学院，管理学院，瓦莱-瓦莱大学应用科学和艺术西瑞士）
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on the dataset, we set up multi-choice and open-ended QA tasks targeting causal action reasoning, temporal action reasoning, and common scene comprehension. Through extensive analysis of baselines and established VideoQA techniques, we find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning. Furthermore, the models that are effective on multi-choice QA, …
Cross-Modal Causal Intervention for Medical Report Generation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东大数据分析与处理重点实验室）；School of Science, Sun Yat-sen University（中山大学理学院）；Hong Kong Polytechnic University（香港理工大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Slovak University of Technology（斯洛伐克理工大学）；Johannes Kepler University（约翰内斯·开普勒大学）
In many practical applications, deep neural networks have been typically deployed to operate as a black box predictor. Despite the high amount of work on interpretability and high demand on the reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of the decision-making process to a human expert operating…
What is Missing? Explaining Neurons Activated by Absent Concepts
Max Planck Institute for Informatics, SIC（马克斯·普朗克信息研究所，SIC）；Department of Computer Science, Technical University of Darmstadt（达姆施塔特技术大学计算机科学系）；Leibniz Institute for Resilience Research（莱比锡韧性研究所）；Institute for Quantitative and Computational Biosciences, Johannes Gutenberg University Mainz（美因茨雅各布·冯·特利尔大学定量与计算生物科学研究所）；University Medical Center Mainz（美因茨大学医学中心）
MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis
Medical Image and Data Analysis, University Hospital of Tübingen, Germany（医学图像与数据分析，图宾根大学医院，德国）；Institute for Signal Processing and System Theory, University of Stuttgart, Germany（信号处理与系统理论研究所，斯图加特大学，德国）；Stanford University, Department of Radiology, Stanford, USA（斯坦福大学放射科，斯坦福，美国）
Imperial College London（帝国理工学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive. Balancing the workload between model development, data collection and annotations is difficult for machine learning practitioners, especially under time constraints. Causal analysis is often used in medicine and economics to gain insights about the effects of actions and policies. In this paper we explore the effect of dataset interventions on the output of image classification models. Through a causal approach we investigate the effects of the quantity and type of data we nee…
UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
Faculty of Computing and IT (FCIT)（计算与信息技术学院）；Sohar University（索哈尔大学）；Department of Intelligent Systems（智能系统系）；KTH Royal Institute of Technology（皇家理工学院）；College of Computer and Information Sciences（计算机与信息科学学院）；Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学）；University of Auckland（奥克兰大学）；Department of Computer Science, Faculty of Science（科学学院计算机科学系）；Northern Border University（北方边境大学）
