Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thus allowing a much larger number of patients to undergo imaging. However, the diagnosis of X-ray images relies heavily on the expertise and experience of physicians, and few machine learning methods have been developed to assist in this process. We propose a novel and practical gastric cancer diagnostic support system for gastric X-ray images that will enable more people to be screened. The system is based on a general deep learning-based object detection model and incorporates two novel techniques: refined probabilistic stomach image augmentation (R-sGAIA) and hard boundary box training (HBBT). R-sGAIA enhances the probabilistic gastric fold region and provides more learning patterns for cancer detection models. HBBT is an efficient training method that improves model performance by allowing the use of unannotated negative (i.e., healthy control) samples, which are typically unusable in conventional detection models. The proposed system achieved a sensitivity (SE) for gastric cancer of 90.2\%, higher than that of an expert (85.5%). Under these conditions, two out of five candidate boxes identified by the system were cancerous (precision = 42.5%), with an image processing speed of 0.51 seconds per image. The system also outperformed methods using the same object detection model and state-of-the-art data augmentation by showing a 5.9-point improvement in the F1 score. In summary, this system efficiently identifies areas for radiologists to examine within a practical time frame, thus significantly reducing their workload.
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics
Institute of Pathology, University Hospital Carl Gustav Carus (UKD), TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院病理研究所）；ASGEN GmbH & Co. KG（ASGEN两合有限责任公司）；National Center for Tumor Diseases (NCT), Partner Site Dresden（国家肿瘤疾病中心（德累斯顿合作站点））；ContextVision AB（康泰视伦公司）；Institute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院医学信息学与生物测量学研究所）；Institute of Bioengineering, School of Life Sciences, EPFL（洛桑联邦理工学院生命科学学院生物工程研究所）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
Hunan University（湖南大学）；University of Ontario Institute of Technology（安大略理工大学）；York University（约克大学）；Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine（上海中医药大学附属龙华医院）
Gastric histopathology image segmentation using a hierarchical conditional random field
Northeastern University（东北大学）；MBIE College（MBIE学院）；Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education（教育部医学影像与智能分析工程研究中心）；Microscopic Image and Medical Image Analysis Group（显微图像与医学图像分析组）
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking University（北京大学）；Hupan Lab（湖畔实验室）；Changsha Institute for Computing and Digital Economy（长沙计算与数字经济研究院）；Guangdong Province People’s Hospital（广东省人民医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）
Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment
KAIST（韩国科学技术院）；Chungnam National University School of Medicine（忠南大学医学院）；Chungnam National University Hospital（忠南大学医院）；Chungnam National University Sejong Hospital（忠南大学世宗医院）
本文提出基于两阶段混合 ViT 与多尺度自注意力的 AI 系统，实现胃癌病理五分类并匹配治疗指南，在多中心切片中验证可提升灵敏度并缩短筛查时间。
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation
Beijing University of Posts and Telecommunications（北京邮电大学）；Center for Research on Intelligent Perception and Computing, NLPR, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；Shandong University of Science and Technology（山东科技大学）；Artificial Intelligence Research, CAS (CAS-AIR)（中国科学院人工智能研究院）
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The Hong Kong University of Science and Technology（香港科技大学）；The Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；Sichuan Province Cancer Hospital, University of Electronic Science and Technology of China（电子科技大学四川省肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images
An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection
Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Biomedical Engineering, The Chinese University of Hong Kong（香港中文大学生物医学工程系）；Department of Neurology, David Geffen School of Medicine, University of California（加州大学洛杉矶分校神经医学系）；Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University（清华大学脑启发计算研究中心）；Department of Gastroenterology, Affiliated Hospital of Hebei University of Engineering（河北工程大学附属医院消化内科）；Institute of Artificial Intelligence, Chongqing Technology and Business University（重庆理工大学人工智能学院）；Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau（澳门大学医学翻译中心癌症中心）；MoE Frontiers Science Center for Precision Oncology, University of Macau（澳门教育暨青年事务局精准肿瘤学前沿科学中心）；The director of the Institute of Clinical Medicine, The First People’s Hospital of Foshan（佛山第一人民医院临床医学研究所主任）；Hebei Key Laboratory of Medical Data Science, Institute of Biomedical Informatics, School of Medicine, Hebei University of Engineering（河北工程大学医学数据科学重点实验室）；The Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（清华大学北京信息科学与技术国家研究中心）
HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation
School of Computer Science and Engineering（计算机科学与工程学院）；School of Future Technology（未来技术学院）；South China University of Technology（华南理工大学）；Department of Medical Imaging（医学影像科）；Guangzhou Institute of Cancer Research（广州癌症研究所）；the Affiliated Cancer Hospital（附属肿瘤医院）；Guangzhou Medical University（广州医学院）；Department of Radiology（放射科）；Meizhou People’s Hospital（梅州市人民医院）；School of Computer Science and Technology（计算机科学与技术学院）；Hainan University（海南大学）；Department of Oncology Institute（肿瘤研究所）
2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Guangzhou First People’s Hospital, School of Medicine, South China University of Technology（华南理工大学医学院附属广州市第一人民医院）；Guizhou Provincial People’s Hospital（贵州省人民医院）；Henan Cancer Hospital（河南省肿瘤医院）；Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院、广东省医学科学院）；Beihang University（北京航空航天大学）
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；University of Oldenburg（奥尔登堡大学）
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation
UCL Hawkes Institute（UCL Hawkes Institute；医学物理与生物医学工程系）；Department of Medical Physics and Biomedical Engineering（大学学院伦敦）；University College London（医学成像中心）；Centre of Medical Imaging（肿瘤学系）；Department of Oncology（阿迦·柯尔大学医院）；Aga Khan University Hospital（生物工程中心；工程与材料科学学院）；Centre for Bioengineering；School of Engineering and Materials Science
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
University of Cambridge（剑桥大学）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）；Medical University Vienna（维也纳医科大学）；Cambridge University Hospitals National Health Service (NHS) Foundation Trust（剑桥大学医院国民保健服务基金会信托）；Eindhoven University of Technology（埃因霍温理工大学）；Leiden University（莱顿大学）；University College London（伦敦大学学院）；Cancer Research UK Cambridge Institute（英国癌症研究中心剑桥研究所）
