Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. Thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our knowledge, no appropriate self-supervised pre-training method has been designed for histopathology HSIs. In this paper, we introduce an efficient and effective Self-supervised Spectral Regression (S$^3$R) method, which exploits the low rank characteristic in the spectral domain of HSI. More concretely, we propose to learn a set of linear coefficients that can be used to represent one band by the remaining bands via masking out these bands. Then, the band is restored by using the learned coefficients to reweight the remaining bands. Two pre-text tasks are designed: (1)S$^3$R-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morphologies; (2)S$^3$R-BR, which regresses the missing band, making the model to learn the holistic semantics of HSIs. Compared to prior arts i.e., contrastive learning methods, which focuses on natural images, S$^3$R converges at least 3 times faster, and achieves significant improvements up to 14% in accuracy when transferring to HSI classification tasks.
University of Technology, Ho Chi Minh City, Vietnam（越南胡志明市技术大学）；Carnegie Mellon University, Pennsylvania, USA（卡内基梅隆大学）；Posts and Telecommunications Institute of Technology, Ho Chi Minh City, Vietnam（越南邮电技术研究所）；Purdue University, Indiana, USA（普渡大学）；University of Alabama at Birmingham, Alabama, USA（阿拉巴马大学伯明翰分校）
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）
HARE: an entity and relation centric evaluation framework for histopathology reports
University College London（伦敦大学学院）；University of Glasgow（格拉斯哥大学）；Human Technopole（人类技术极地）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院国家健康服务信托）
本文提出以实体和关系为中心的组织病理学报告评估框架 HARE，通过构建标注基准、微调 NER 与 RE 模型并设计新指标，在专家评估相关性上优于传统及放射学指标。
Automatic and explainable grading of meningiomas from histopathology images
Technische Hochschule Ingolstadt（英戈尔施塔特工业大学）；University Hospital Erlangen（埃尔朗根大学医院）；University of Veterinary Medicine Vienna（维也纳兽医大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
McMaster University（麦克马斯特大学）；Mayo Clinic（梅奥诊所）；William Osler Health System（威廉·奥斯勒医疗系统）
H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
Institute of Information Systems, HES-SO (University of Applied Sciences and Arts Western Switzerland)（瑞士西部应用科学与艺术大学 信息系统研究所）；Department of Computer Science, University of Geneva（日内瓦大学 计算机科学系）；Department of Neurosciences, University of Padua（帕多瓦大学 神经科学系）；Inselspital, Bern Hospital（伯尔尼大学医院）；Medical Faculty, University of Geneva（日内瓦大学 医学院）
A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images
Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电气与计算机工程系）；Seed Production Innovation, Crop Science Division, Bayer Company（拜耳公司种子生产创新部）；Division of Medicine – Quantitative Health, University of Florida（佛罗里达大学医学部-定量健康分部）；Department of Health Outcomes and Biomedical Informatics, University of Florida（佛罗里达大学健康结果与生物医学信息学系）；Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine（佛罗里达大学医学院病理学、免疫学与实验室医学系）；Kidney Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health（美国国立卫生研究院糖尿病、消化系统与肾病研究所肾病分支）；Indiana University School of Medicine（印第安纳大学医学院）；Departments of Medicine, Washington University School of Medicine（华盛顿大学医学院医学部）；Universidade de Coimbra（科英布拉大学）；Department of Pathology and Laboratory Medicine, University of California at Davis School of Medicine（加州大学戴维斯分校医学院病理学与实验室医学系）；Department of Pathology, Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院病理学系）
Analyzing Near-Infrared Hyperspectral Imaging for Protein Content Regression and Grain Variety Classification Using Bulk References and Varying Grain-to-Background Ratios
University of Copenhagen（哥本哈根大学）；FOSS Analytical A/S（福斯分析仪器公司）
A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay（印度理工学院孟买分校资源工程研究中心）；LISTIC, Université Savoie Mont-Blanc（萨瓦勃朗峰大学LISTIC实验室）；Indian Institute of Remote Sensing, ISRO（印度空间研究组织印度遥感研究所）
SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
Department of Data Science and Artificial Intelligence, School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University（双威大学工程与技术学院计算与人工智能学院数据科学与人工智能系）；Faculty of Dentistry, Universiti Malaya（马来亚大学牙科学院）
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Else Kroener Fresenius Center for Digital Health（埃尔瑟·克勒纳·弗雷森纽斯数字健康中心）；TUD Dresden University of Technology（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院及大学医院）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州））；IBM Research Europe（IBM欧洲研究院）；Centre de Recherche des Cordeliers（科尔得利研究所）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎城市大学）；Assistance Publique-Hôpitaux de Paris（巴黎公共医疗援助中心）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；German Cancer Consortium (DKTK)（德国癌症联盟）；University Hospital Heidelberg（海德堡大学医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Leeds（利兹大学）
Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach
FOSS Analytical A/S（福斯分析公司）；Department of Computer Science (DIKU), University of Copenhagen（哥本哈根大学计算机科学系）；Department of Food Science (UCPH FOOD), University of Copenhagen（哥本哈根大学食品科学系）；Information Technology Group, Wageningen University and Research (WUR)（瓦赫宁根大学及研究中心信息技术组）；Food and Biobased Research, Wageningen University and Research (WUR)（瓦赫宁根大学及研究中心食品与生物基研究）；Natural History Museum of Denmark (NHMD), University of Copenhagen（哥本哈根大学丹麦自然历史博物馆）
Investigating the Applicability of a Snapshot Computed Tomography Imaging Spectrometer for the Prediction of Brix and pH of Grapes
University of Southern Denmark（南丹麦大学）；Newtec Engineering A/S（纽泰克工程公司）；SDU NanoSyd, Mads Clausen Institute, University of Southern Denmark（南丹麦大学南校区纳米科学中心、马德森·克劳森研究所）；Department of Physics, Chemistry and Pharmacy, University of Southern Denmark（南丹麦大学物理、化学与药学院）；SDU Climate Cluster, the University of Southern Denmark（南丹麦大学SDU气候集群）
Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS Grenoble INP（法国国家科学研究中心格勒诺布尔国立理工学院）；GIPSA-Lab（格勒诺布尔图像、信号、声学与自动化实验室）；Institut Universitaire de France (IUF)（法国大学研究院）
