In this paper, we present XctDiff, an algorithm framework for reconstructing CT from a single radiograph, which decomposes the reconstruction process into two easily controllable tasks: feature extraction and CT reconstruction. Specifically, we first design a progressive feature extraction strategy that is able to extract robust 3D priors from radiographs. Then, we use the extracted prior information to guide the CT reconstruction in the latent space. Moreover, we design a homogeneous spatial codebook to improve the reconstruction quality further. The experimental results show that our proposed method achieves state-of-the-art reconstruction performance and overcomes the blurring issue. We also apply XctDiff on self-supervised pre-training task. The effectiveness indicates that it has promising additional applications in medical image analysis. The code is available at:https://github.com/qingze-bai/XctDiff
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
Department of Radiology, Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin（柏林查理医院放射科，弗赖堡大学柏林分校和洪堡大学成员）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（阿提诺拉A.马丁努斯生物医学成像中心，麻省总医院和哈佛医学院）；Department of Gastroenterology, Infectious Diseases and Rheumatology (incl. nutrition medicine), Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin（胃肠病学、传染病学和风湿病学（含营养医学）部门，柏林查理医院，弗赖堡大学柏林分校和洪堡大学成员）；Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich（诊断和介入放射科，医学院，慕尼黑技术大学）
AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Faculty of Engineering, University of Peradeniya（工程学院，珀德尼亚大学）；Faculty of Dental Sciences, University of Peradeniya（牙科学院，珀德尼亚大学）；Simula Metropolitan Center for Digital Engineering（模拟 Metropolitan 数字工程中心）
Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis
Center for Computational & Data Sciences, Independent University, Bangladesh（孟加拉国独立大学计算与数据科学中心）；Department of Computer Science and Engineering, Independent University, Bangladesh（孟加拉国独立大学计算机科学与工程系）；Advanced Medical Engineering Research Institute, University of Hyogo, Japan（日本兵库大学先进医学工程研究所）；Graduate School of Engineering, University of Hyogo, Japan（日本兵库大学工学研究科）
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）
Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practitioner suffers from subjectivity and is highly dependent on the experience. Computer-aided diagnostics (CAD) could improve the objectivity of knee radiographic examination. The first essential step of knee OA CAD is to automatically localize the joint area. However, according to the literature this task itself remains challenging. The aim of this study was to develop novel and computationally efficient method to tack…
Deep Learning-based Alignment Measurement in Knee Radiographs
Division of Informatics, Imaging and Data Sciences, The University of Manchester（信息学与数据科学系，曼彻斯特大学）；Northern Care Alliance NHS Foundation Trust（北护理联盟国家健康服务信托基金）；Department of Trauma and Orthopaedics, Stockport NHS Foundation Trust（创伤与骨科部门，史托波特国家健康服务信托基金）；School of Health and Society, University of Salford（健康与社会学院，萨尔福德大学）；School of Biological Sciences, The University of Manchester（生物科学学院，曼彻斯特大学）；Research Unit of Health Sciences and Technology, University of Oulu（健康科学与技术研究单位，奥卢大学）
School of Computing and Information Technology, University of Wollongong（卧龙岗大学计算与信息技术学院）
The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large number of people worldwide, posing significant stress to the health care systems. Early and accurate diagnosis of COVID-19 is critical in screening of infected patients and breaking the person-to-person transmission. Chest X-ray (CXR) based computer-aided diagnosis of COVID-19 using deep learning becomes a promising solution to this end. However, the diverse and various radiographic features of COVID-19 make it challenging, especially when considering each CXR scan typically only generates one single image. Data scarcity i…
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh Medical School（匹兹堡大学医学院）
We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain "how" imaging features in important anatomical regions are relevant to the classification decision. Our framework explains the decision for a target class by gradually "exaggerating" the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision ch…
Deep Learning Models to Automate the Scoring of Hand Radiographs for Rheumatoid Arthritis
Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）；Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford（牛津大学纳菲尔德骨科、风湿病学与肌肉骨骼科学系）
Interpretable Rheumatoid Arthritis Scoring via Anatomy-aware Multiple Instance Learning
Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK（大数据研究所，人口健康系，牛津大学，牛津，英国）；Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK（骨科、风湿病学和肌肉骨骼科学系，牛津大学，牛津，英国）
DeBoneDiT: Depth-Driven Conditional Bridge Diffusion Transformers for Bone Suppression in Chest X-Ray Images
Zhejiang University（浙江大学）；Hangzhou Dianzi University（杭州电子科技大学）；City University of Hong Kong（香港城市大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（国家癌症中心/国家临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院深圳医院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs
Division of Informatics, Imaging and Data Sciences, The University of Manchester（曼彻斯特大学信息学、影像与数据科学部）；Research Unit of Health Sciences and Technology, University of Oulu（奥卢大学健康科学与技术研究部）；Medical Research Center Oulu, University of Oulu and Oulu University Hospital（奥卢大学与奥卢大学医院医学研究中心）；Department of Trauma and Orthopaedics, Stockport NHS Foundation Trust, Stepping Hill Hospital（斯泰平希尔医院斯托克波特NHS基金会创伤与骨科）；School of Health and Society, University of Salford（索尔福德大学健康与社会学院）；School of Biological Sciences, The University of Manchester（曼彻斯特大学生物科学学院）；Weill Cornell Medicine, Cornell University（康奈尔大学威尔康奈尔医学院）
Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis
Orthopaedic Department, Faculty of Medicine UIN Syarif Hidayatullah Jakarta（乌姆尼大学医学学院骨科部）；Informatics Engineering, Institut Teknologi Sepuluh Nopember（十月份技术研究所信息工程系）；Information Technology, Universitas Muhammadiyah Yogyakarta（尤科阿卡塔大学信息技术系）；Industrial and Systems Engineering, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学工业与系统工程系）
University of Sydney（悉尼大学）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
'Radiomics' is a method that extracts mineable quantitative features from radiographic images. These features can then be used to determine prognosis, for example, predicting the development of distant metastases (DM). Existing radiomics methods, however, require complex manual effort including the design of hand-crafted radiomic features and their extraction and selection. Recent radiomics methods, based on convolutional neural networks (CNNs), also require manual input in network architecture design and hyper-parameter tuning. Radiomic complexity is further compounded when there are multiple…
