This study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning. Although supervised deep learning has achieved huge success across various medical image analysis tasks, its success relies on large amounts of high-quality annotations, which can be challenging to acquire in practice. To overcome this limitation, we propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to leverage and learn useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss aims at enhancing model robustness against virtual adversarial perturbation, thus improving model generalizability. To evaluate the performance of our VAT-based CAD scheme, we retrospectively assembled a total of 1024 breast mass images, with equal number of benign and malignant masses. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740 and 0.760, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images.
Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement
The First Affiliated Hospital of Chongqing Medical University（重庆医科大学附属第一医院）；Chongqing Municipal Health Commission（重庆市卫生健康委员会）；Chongqing Translational Medicine Center（重庆市转化医学中心）
Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；Leicester Cancer Research Centre, University of Leicester（莱斯特大学莱斯特癌症研究中心）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Nuffield Department of Medicine, University of Oxford（牛津大学纳菲尔德医学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）；ZoyMed（佐医医疗（ZoyMed））
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Universitat de Barcelona（巴塞罗那大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；Karolinska Institutet（卡罗林斯卡学院）；King’s College London（伦敦国王学院）
SpurBreast: A Curated Dataset for Investigating Spurious Correlations in Real-world Breast MRI Classification
Center for Biosystems；Biotech Data Science, Ghent University Global Campus, Incheon, Republic of Korea IDLab, ELIS, Ghent University, Ghent, Belgium Department of Applied Mathematics, Computer Science；Statistics, Ghent University, Ghent, Belgium ( )
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran；Department of Computer Engineering Techniques, Mazaya University College, Nasiriyah, Iraq；Department of Medicine, Danube Private University, Krems an der Donau, Austria；Austrian Center for Medical Innovation；Research Center for Medical Image Analysis；Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
Department of Radiology, Fondazione Policlinico Campus Bio-Medico（放射学系，政策临床医学院）；Department of Radiology, Università Campus Bio-Medico di Roma（放射学系，罗马生物医学大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入系，辐射物理，生物医学工程，乌梅拉大学）
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；Bioinformatics Institute, A*STAR（新加坡科技研究局生物信息学研究所）；Hospital Universitari Sant Joan de Reus（雷乌斯圣琼大学医院）
寻找乳房肿瘤的乳房X光检查是放射科医生必须经常进行的一项艰巨任务。因此，需要图像分析方法来检测和描绘乳腺肿块，其描绘将支持可靠诊断的关键形态信息。在本文中，我们提出了一个条件生成对抗网络（cGAN）的目的是分割乳腺肿块内的一个区域的兴趣（ROI）在乳房X光片。生成网络学习识别乳房肿块区域并创建勾勒乳房肿块的二进制掩模。反过来，对抗网络学会区分真实（地面真实）和合成分割，从而强制生成网络创建尽可能真实的二进制掩码。即使在训练样本数量有限的情况下，cGAN也能很好地工作。因此，所提出的方法优于几个国家的最先进的方法。这一假设得到了在两个数据集上进行的各种实验的证实，这两个数据集是公共的INbreast和私人的内部数据集。所提出的分割模型分别提供了94%和87%的高Dice系数和Intersection over Union（IoU）。此外，提出了一种基于卷积神经网络（CNN）的形状描述符，将生成的掩模分为四种形状：不规则，小叶，椭圆形和圆形。建议的形状描述符进行训练的数字数据库筛选乳腺摄影（DDSM）产生的总体准确率为80%，这优于目前的最先进的。
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM)（巴塞罗那人工智能医学实验室）；Departament de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与计算机科学系）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（生物医学成像机器学习研究所，海德堡慕尼黑）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Department of Oncology-Pathology, Karolinska Institutet（卡罗林斯卡研究所肿瘤-病理学系）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education
Faculty of Medicine, American University of Beirut（贝鲁特美国大学医学院）；American University of Beirut Medical Center（贝鲁特美国大学医学中心）；Department of Diagnostic Radiology, American University of Beirut Medical Center（贝鲁特美国大学医学中心诊断放射科）
在他们的成长期，放射学实习生被要求每月解读数百张乳房X光片，目的是能够辨别出区分良性和恶性病变的细微模式。不幸的是，医学法律和技术障碍使得难以访问和查询用于训练的医学图像。在本文中，我们训练一个生成对抗网络（GAN）来合成512 x 512的高分辨率乳房X线照片。所得到的模型导致高级别特征（例如，标准乳房X线摄影视图和乳房病变的性质）的无监督分离，在所生成的图像中具有随机变化（例如，乳房脂肪组织、钙化），从而实现合成图像的用户控制的全局和局部属性编辑。我们证明了该模型的能力，以生成解剖学和医学相关的乳房X线照片，实现了0.54的平均AUC在双盲研究中的四个专家乳房X线摄影放射科医生，以区分生成的和真实的图像，归因于高视觉质量的合成和编辑的乳房X线照片，并在推进和促进医学教育的潜在用途。
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
DEIM, Universitat Rovira i Virgili, Spain（西班牙塔拉戈纳大学（DEIM））；Imaging Informatics Division, Bioinformatics Institute, Singapore（新加坡生物信息学研究所成像信息学部门）；Kayakalp Hospital, 110084 New Delhi, India（印度新德里Kayakalp医院）；Hospital Universitari Sant Joan de Reus, Spain（西班牙圣胡安·德雷斯医院）
Lesion-Aware Generative Artificial Intelligence for Virtual Contrast-Enhanced Mammography in Breast Cancer
Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome（罗马大学生物医学校园计算机系统与生物信息学单位）；Department of Radiology, Fondazione Policlinico Campus Bio-Medico（生物医学校园放射科）；Department of Radiology, Università Campus Bio-Medico di Roma（罗马大学生物医学校园放射科）；Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University（乌梅大学辐射科学、辐射物理、生物医学工程部门）
ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction
Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands（拉德堡德大学医学中心医学影像科，奈梅亨，荷兰）；The Netherlands Cancer Institute, Amsterdam, The Netherlands（荷兰癌症研究所，阿姆斯特丹，荷兰）；Faculty of Applied Sciences, Macao Polytechnic University, Macau, China（澳门理工大学应用科学学院，澳门，中国）；Boston Children’s Hospital, Harvard Medical School, Boston, USA（波士顿儿童医院，哈佛医学院，波士顿，美国）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China（中国科学院香港创新研究院人工智能与机器人创新中心，香港，中国）；Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands（乌得勒支大学医学中心影像部，乌得勒支，荷兰）；Department of Radiology, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China（福州大学附属省立医院放射科，福州，中国）；Department of Radiology, Yantai Yuhuangding Hospital, Shandong, China（烟台毓璜顶医院放射科，山东，中国）
Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets
Zhejiang cancer hospital（浙江省肿瘤医院）；Shanghai Jiao Tong University（上海交通大学）；Eindhoven University of Technology（埃因霍温理工大学）；Tokyo Institute of Technology（东京工业大学）；Yale University（耶鲁大学）；German Research Center of Artificial Intelligence（德国人工智能研究中心）
DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（中山大学附属肿瘤医院放射科）；Medical Imaging Center, Shenzhen Hospital of Southern Medical University（南方医科大学深圳医院医学影像中心）；Hithink RoyalFlush Information Network Co., Ltd.（慧思科技有限公司）；Department of Radiology, The third Affilliated Hospital of Guangzhou Medical University（广州医学院第三附属医院放射科）
Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification
Center for Biosystems and Biotech Data Science, Ghent University Global Campus（根特大学全球校区生物系统与生物科技数据科学中心）；The State University of New York Korea（韩国州立纽约大学）；Ghent University（根特大学）
Regularization with Latent Space Virtual Adversarial Training
Philips Co-Creation Center（飞利浦共创中心）；University of Tsukuba（筑波大学）；I Dragon Corporation（I Dragon公司）；The Tokyo Foundation for Policy Research（东京政策研究基金会）；Lowe’s Services India Pvt. Ltd.（劳氏印度服务私人有限公司）
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection
Yu-Yue Pathology Research Center（渝越病理研究中心）；Jinfeng Laboratory（金凤实验室）；Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Army Medical University (Third Military Medical University)（陆军军医大学（第三军医大学）西南医院病理科及西南癌症中心）
Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining
INESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal（INESC TEC，工程学院， Porto 大学，Porto，葡萄牙）；The Netherlands Cancer Institute, Amsterdam, The Netherlands（荷兰癌症研究所，阿姆斯特丹，荷兰）；IMP Diagnostics, Porto, Portugal（IMP 诊断公司，Porto，葡萄牙）
Memory-aware curriculum federated learning for breast cancer classification
Universitat Pompeu Fabra（庞培法布拉大学）；IT University of Copenhagen（哥本哈根信息技术大学）；ICREA（加泰罗尼亚研究与高级研究院）；Ecole Centrale de Nantes（南特中央理工学院）；Hera-MI SAS（Hera-MI公司）
