Advances in artificial intelligence (AI) show great potential in revealing underlying information from phonon microscopy (high-frequency ultrasound) data to identify cancerous cells. However, this technology suffers from the 'batch effect' that comes from unavoidable technical variations between each experiment, creating confounding variables that the AI model may inadvertently learn. We therefore present a multi-task conditional neural network framework to simultaneously achieve inter-batch calibration, by removing confounding variables, and accurate cell classification of time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22% and an average cross-validated precision of 89.07% for classifying background, healthy and cancerous regions. Classification can be performed in 0.5 seconds with only simple prior batch information required for multiple batch corrections. Further, we extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state including sound velocity, sound attenuation and cell-adhesion to substrate.
Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation
School of Computer Science and Enginecring, Central South University（计算机科学与工程学院，中南大学）；School of Computer Science（计算机科学学院）；Enginecring, Central South University（工程学院，中南大学）
Deep Learning to Predict Late-Onset Breast Cancer Metastasis: the Single Hyperparameter Grid Search (SHGS) Strategy for Meta Tuning Concerning Deep Feed-forward Neural Network
Self-learned representation-guided latent diffusion model for breast cancer classification in deep ultraviolet whole surface images
Department of Computer Science, Georgia State University（计算机科学系，佐治亚州立大学）；Department of Electrical and Computer Engineering, Marquette University（电气与计算机工程系，马基特大学）；Joint Dept. of Biomedical Eng., Marquette Univ. and Med. Coll. of Wisconsin（马基特大学与威斯康星医学院联合生物医学工程系）
Search for temporal cell segmentation robustness in phase-contrast microscopy videos
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；AtlasXomics Inc.（AtlasXomics 公司）；Albert-Ludwigs-University（阿尔伯特-路德维希大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）
TransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals
Institute for Biomedical Informatics, Faculty of Medicine；University Hospital Cologne, University of Cologne, Cologne, Germany.；Faculty of Mathematics；Natural Sciences, University of Cologne, Cologne, Germany.；Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine；Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany；Charité Comprehensive Cancer Center, Charité Universitätsmedizin, Berlin, Germany.；Centre for Genomic Regulation, Barcelona, Spain.
