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Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial

arXiv

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arXiv 2024年

作者： Zhong, Xian Salahuddin, Zohaib Chen, Yi Woodruff, Henry C. Long, Haiyi Peng, Jianyun Udawatte, Nuwan Casale, Roberto Mokhtari, Ayoub Zhang, Xiaoer Huang, Jiayao Wu, Qingyu Tan, Li Chen, Lili Li, Dongming Xie, Xiaoyan Lin, Manxia Lambin, Philippe The D-Lab Department of Precision Medicine GROW - School for Oncology Maastricht University Maastricht Netherlands Department of Radiology and Nuclear Medicine GROW - School for Oncology Maastricht University Medical Center+ Maastricht Netherlands Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound The First Affiliated Hospital of Sun Yat-sen University Guangzhou China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis College of Computer Science and Technology Guizhou University Guiyang China Radiology Department Institut Jules Bordet Hôpital Universitaire de Bruxelles Université Libre de Bruxelles Brussels1070 Belgium Center of Hepato-Pancreato-Biliary Surgery The First Affiliated Hospital of Sun Yat-sen University Guangzhou China Department of Pathology The First Affiliated Hospital of Sun Yat-sen University Guangzhou China

Artificial intelligence (AI)-based decision support systems have demonstrated value in predicting post-hepatectomy liver failure (PHLF) in hepatocellular carcinoma (HCC). However, they often lack transparency, and the impact of model explanations on clinicians' decisions has not been thoroughly evaluated. Building on prior research, we developed a variational autoencoder-multilayer perceptron (VAE-MLP) model for preoperative PHLF prediction. This model integrated counterfactuals and layerwise relevance propagation (LRP) to provide insights into its decision-making mechanism. Additionally, we proposed a methodological framework for evaluating the explainability of AI systems. This framework includes qualitative and quantitative assessments of explanations against recognized biomarkers, usability evaluations, and an in silico clinical trial. Our evaluations demonstrated that the model's explanation correlated with established biomarkers and exhibited high usability at both the case and system levels. Furthermore, results from the three-track in silico clinical trial showed that clinicians' prediction accuracy and confidence increased when AI explanations were provided. © 2024, CC BY-NC-SA.

关键词： Prediction models

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Sequential 3D Human Pose and Shape Estimation From Point Clouds

Sequential 3D Human Pose and Shape Estimation From Point Clo...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kangkan Wang Jin Xie Guofeng Zhang Lei Liu Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China ZJU-SenseTime Joint Lab of 3D Vision

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

This work addresses the problem of 3D human pose and shape estimation from a sequence of point clouds. Existing sequential 3D human shape estimation methods mainly focus on the template model fitting from a sequence of depth images or the parametric model regression from a sequence of RGB images. In this paper, we propose a novel sequential 3D human pose and shape estimation framework from a sequence of point clouds. Specifically, the proposed framework can regress 3D coordinates of mesh vertices at different resolutions from the latent features of point clouds. Based on the estimated 3D coordinates and features at the low resolution, we develop a spatial-temporal mesh attention convolution (MAC) to predict the 3D coordinates of mesh vertices at the high resolution. By assigning specific attentional weights to different neighboring points in the spatial and temporal domains, our spatial-temporal MAC can capture structured spatial and temporal features of point clouds. We further generalize our framework to the real data of human bodies with a weakly supervised fine-tuning method. The experimental results on SURREAL, Human3.6M, DFAUST and the real detailed data demonstrate that the proposed approach can accurately recover the 3D body model sequence from a sequence of point clouds.

关键词： Three-dimensional displays Solid modeling Shape Biological system modeling Estimation Spatial resolution Feature extraction

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Integrating Dense LiDAR-Camera Road Detection Maps by a Multi-Modal CRF Model

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IEEE Transactions on Vehicular technology 2019年第12期68卷 11635-11645页

作者： Gu, Shuo Zhang, Yigong Tang, Jinhui Yang, Jian Alvarez, Jose M. Kong, Hui PCA Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Sch. of Comp. Sci. and Engineering Nanjing Univ. of Sci. and Technol. Nanjing 210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection method via a LiDAR-camera fusion strategy to exploit both the range and color information. The whole system consists of three parts. In the LiDAR based part, we transform the discrete 3D LiDAR point clouds to continuous 2D LiDAR range images and propose a distance-aware height-difference based scanning approach to get the road estimations quickly. In the camera based part, we apply a light-weight transfer learning based road segmentation network. In the LiDAR-camera fusion part, we transform the detection results from LiDAR and camera to dense and binary ones to solve the data imbalance problem and fuse them in a multi-modal conditional random field (MM-CRF) framework. Experiments show that the proposed MM-CRF fusion method can operate in real-time and achieve competitive performance compared with the state-of-the-art road detection algorithms on the KITTI-Road benchmark. © 1967-2012 IEEE.

关键词： KITTI LiDAR-camera fusion road detection

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Adversarial refinement network for human motion prediction

arXiv

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arXiv 2020年

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong Hong Kong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2020, CC-BY.

关键词： Errors

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BoostTree and BoostForest for Ensemble Learning

arXiv

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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

arXiv

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arXiv 2022年

作者： Chen, Zhifeng Liao, Congyu Cao, Xiaozhi Poser, Benedikt A. Xu, Zhongbiao Lo, Wei-Ching Wen, Manyi Cho, Jaejin Tian, Qiyuan Wang, Yaohui Feng, Yanqiu Xia, Ling Chen, Wufan Liu, Feng Bilgic, Berkin School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia Department of Radiology Stanford University Stanford CA United States Maastricht Brain Imaging Center Faculty of Psychology and Neuroscience University of Maastricht Netherlands Department of Radiotherapy Cancer Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Science Guangzhou China Siemens Medical Solutions BostonMA United States Department of Chemical Pathology The Chinese University of Hong Kong Hong Kong Division of Superconducting Magnet Science and Technology Institute of Electrical Engineering Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D GRE-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T2* mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance the image quality. For T2* mapping, T2* values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T2* mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3 T and 9 s on a 7 T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T2* mapping in 47 s at 1.1×1.1×1.0 mm3 resolution. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Multi-modal remote sensory learning for multi-objects over autonomous devices

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Frontiers in bioengineering and biotechnology 2025年 13卷 1430222页

作者： Aysha Naseer Naif Almudawi Hanan Aljuaid Abdulwahab Alazeb Yahay AlQahtani Asaad Algarni Ahmad Jalal Hui Liu Department of Computer Science Air University Islamabad Pakistan. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia. Department of Computer Science College of Computer Science and Information System Najran University Najran Saudi Arabia. Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia. Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia. Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea. Guodian Nanjing Automation Co. Ltd. Nanjing China. Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and technology Nanjing China. Cognitive Systems Lab University of Bremen Bremen Germany.

Introduction:There has been an increasing focus on object segmentation within remote sensing images in recent years due to advancements in remote sensing technology and the growing significance of these images in both military and civilian realms. In these situations, it is critical to accurately and quickly identify a wide variety of objects. In many computer vision applications, scene recognition in aerial-based remote sensing imagery presents a common issue. Method:However, several challenging elements make this work especially difficult: (i) Different objects have different pixel densities; (ii) objects are not evenly distributed in remote sensing images; (iii) objects can appear differently depending on viewing angle and lighting conditions; and (iv) there are fluctuations in the number of objects, even the same type, in remote sensing images. Using a synergistic combination of Markov Random Field (MRF) for accurate labeling and Alex Net model for robust scene recognition, this work presents a novel method for the identification of remote sensing objects. During the labeling step, the use of MRF guarantees precise spatial contextual modeling, which improves comprehension of intricate interactions between nearby aerial objects. By simultaneously using deep learning model, the incorporation of Alex Net in the following classification phase enhances the model's capacity to identify complex patterns in aerial images and adapt to a variety of object attributes. Results:Experiments show that our method performs better than others in terms of classification accuracy and generalization, indicating its efficacy analysis on benchmark datasets such as UC Merced Land Use and AID. Discussion:Several performance measures were calculated to assess the efficacy of the suggested technique, including accuracy, precision, recall, error, and F1-Score. The assessment findings show a remarkable recognition rate of around 97.90% and 98.90%, on the AID and the UC Merced Land datasets,

关键词： autonomous devices deep learning computer vision likelihood estimation multi-modal multi-objects posterior probability remote sensing scene analysis

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Learning local neighboring structure for robust 3D shape representation

arXiv

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arXiv 2020年

作者： Gao, Zhongpai Yan, Junchi Zhai, Guangtao Zhang, Juyong Yang, Yiyan Yang, Xiaokang MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China School of Mathematical Sciences University of Science and Technology of China China

Mesh is a powerful data structure for 3D shapes. Representation learning for 3D meshes is important in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e.g., images) suggests the value of adapting insight from CNN for 3D shapes. However, 3D shape data are irregular since each node's neighbors are unordered. Various graph neural networks for 3D shapes have been developed with isotropic filters or predefined local coordinate systems to overcome the node inconsistency on graphs. However, isotropic filters or predefined local coordinate systems limit the representation power. In this paper, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each node according to the local neighboring structure and performs shared anisotropic filters. In fact, the learnable weighting matrix is similar to the attention matrix in the random synthesizer - a new Transformer model for natural language processing (NLP). Comprehensive experiments demonstrate that our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Minimal Solutions to Relative Pose Estimation From Two Views Sharing a Common Direction With Unknown Focal Length

Minimal Solutions to Relative Pose Estimation From Two Views...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yaqing Ding Jian Yang Jean Ponce Hui Kong Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology INRIA Paris France Département d'informatique de l'ENS ENS CNRS PSL University Paris France

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We propose minimal solutions to relative pose estimation problem from two views sharing a common direction with unknown focal length. This is relevant for cameras equipped with an IMU (inertial measurement unit), e.g., smart phones, tablets. Similar to the 6-point algorithm for two cameras with unknown but equal focal lengths and 7-point algorithm for two cameras with different and unknown focal lengths, we derive new 4- and 5-point algorithms for these two cases, respectively. The proposed algorithms can cope with coplanar points, which is a degenerate configuration for these 6- and 7-point counterparts. We present a detailed analysis and comparisons with the state of the art. Experimental results on both synthetic data and real images from a smart phone demonstrate the usefulness of the proposed algorithms.

关键词： Cameras Gravity Eigenvalues and eigenfunctions Smart phones Computer vision Pose estimation Charge coupled devices

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Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats (vol 16, 585, 2025)

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NATURE COMMUNICATIONS 2025年第1期16卷 1-15页

作者： Steinfeldt, Jakob Wild, Benjamin Buergel, Thore Pietzner, Maik Upmeier zu Belzen, Julius Vauvelle, Andre Hegselmann, Stefan Denaxas, Spiros Hemingway, Harry Langenberg, Claudia Landmesser, Ulf Deanfield, John Eils, Roland Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany Institute of Cardiovascular Sciences University College London London UK Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany Institute of Health Informatics University College London London UK British Heart Foundation Data Science Centre London UK Health Data Research UK London UK National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA Pattern Recognition and Image Analysis Lab University of Münster Münster Germany

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

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