Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via perturbations around single data points. In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and unlabeled data, at both the input and latent space in order to regularize different portions of the network. We evaluated the presented model on two distinct medical image data sets for semi-supervised classification of thoracic disease and skin lesion, demonstrating its improved performance over SSL with local perturbations and SSL with global mixing but at the input space only. Our code is available at https://github.com/Prasanna1991/LatentMixing.
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Harbin Institute of Technology, China（哈尔滨工业大学）；Nanjing University of Information Science and Technology, China（南京信息工程大学）；Dalian University of Technology, China（大连理工大学）；Nanyang Technological University, Singapore（南洋理工大学）
What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning, Technical University of Munich（慕尼黑工业大学慕尼黑机器学习中心）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河右岸医院）；ELLIS Institute Finland（芬兰ELLIS研究所）；Aalto University（阿尔托大学）；Imperial College London（伦敦帝国理工学院）
Prompt-driven Latent Domain Generalization for Medical Image Classification
Monash University（莫纳什大学）；Airdoc-Monash Research, Monash University（Airdoc-莫纳什研究，莫纳什大学）；Inception Institute of AI（Inception人工智能研究院）；University of Queensland Diamantina Institute, Dermatology Research Centre, The University of Queensland（昆士兰大学Diamantina研究所，皮肤病学研究中心，昆士兰大学）；Victorian Melanoma Service, Alfred Health（维多利亚黑色素瘤服务中心，阿尔弗雷德健康中心）
Self-supervised learning of inverse problem solvers in medical imaging
Technion（以色列理工学院）；University of Waterloo（滑铁卢大学）
在过去的几年里，基于深度学习的方法在解决医学成像中的逆问题方面取得了巨大的成功。在这项工作中，我们解决了以下问题：\textit{Given a set of measurements obtained from real imaging experiments, what is the best way to use a learnable model and the physics of the modality to solve the inverse problem and reconstruct the latent image?}标准的监督学习为基础的方法来解决这个问题，通过收集已知的潜像和它们相应的测量数据集。然而，由于缺乏适当大小的训练集的可用性，并且更一般地，由于测量“地面实况”潜像的固有困难，这些方法通常是不切实际的。鉴于此，我们提出了一种自监督的方法来训练医学成像中的逆模型，在对齐的数据的情况下。我们的方法只需要在训练时访问测量值和前向模型。我们展示了它的有效性，在加速磁共振成像（MRI）中出现的逆问题。
Latents of latents to delineate pixels: hybrid Matryoshka autoencoder-to-U-Net pairing for segmenting large medical images in GPU-poor and low-data regimes
School of Mathematics and Computer Science, Institute of Business Administration（商业管理学院数学与计算机科学学院）
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
McGill University（麦吉尔大学）；Centre for Intelligent Machines（智能机器中心）；Mila – Quebec AI Institute（魁北克人工智能研究所）；University of Copenhagen（哥本哈根大学）；Department of Public Health, Section for Health Data Science and AI（公共卫生系，健康数据科学与人工智能部门）
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio（电气与信息工程系（DIEI），卡斯诺和南部拉齐亚大学）；Diagnostic Image Analysis Group, Radboud University Medical Center（影像诊断分析组，拉德堡德大学医学中心）；Department of Medical Imaging, Radboud University Medical Center（医学成像系，拉德堡德大学医学中心）
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右侧伊萨尔医院）；Institute for Artificial Intelligence and Computer Science in Medicine, Technical University of Munich（慕尼黑工业大学人工智能与医学计算机科学研究所）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；AI for Image-Guided Diagnosis and Therapy, Technical University of Munich（慕尼黑工业大学图像引导诊断与治疗人工智能研究所）
Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution
Massachusetts Institute of Technology（麻省理工学院）；Department of Biomedical Engineering, Mbarara University of Science and Technology（姆巴拉大学科学与技术学院生物医学工程系）；Johns Hopkins University（约翰霍普金斯大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Makerere University（Makerere大学）；Federal University of São Paulo（圣保罗联邦大学）；Technical University of Applied Sciences Lübeck (TH Lübeck)（吕贝克应用技术大学）；Harvard T.H. Chan School of Public Health（哈佛大学T.H. Chan公共卫生学院）
Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Department of Electrical and Computer Engineering（电子与计算机工程系）；Sungkyunkwan University（成均馆大学）；Center for Neuroscience Imaging Research（神经科学成像研究中心）；Institute for Basic Science（基础科学研究所）；Department of Artificial Intelligence（人工智能系）
hZACH-ViT: Curved Latent Geometry for Compact Vision Transformers in Low-Data Medical Imaging
BioML Lab, Research Institute CODE, UniBw, Munich, Germany（BioML实验室，CODE研究机构，UniBw，慕尼黑，德国）；Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, Netherlands（流行病学与数据科学系，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）
AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $α$-Corrected Binary Cross Entropy and Factorized Latent Supervision
Peter Munk Cardiac Centre, University Health Network (UHN)（彼得·芒克心脏中心，大学健康网络（UHN））；Ted Rogers Centre for Heart Research（泰德·罗杰斯心脏研究中心）；University of Toronto (U of T)（多伦多大学）；Vector Institute（向量研究所）；Toronto General Hospital Research Institute, UHN（多伦多总医院研究所，大学健康网络（UHN））
Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification
Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Ruijin Hospital（瑞金医院）；Fudan University（复旦大学）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属省立医院）；Air Force Medical University（空军军医大学）
