Switching Loss for Generalized Nucleus Detection in Histopathology
Indian Institute of Technology Bombay（印度理工学院孟买分校）；National Institute of Technology Raipur（印度国立技术学院赖布尔分校）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Deepak Anand, Gaurav Patel, Yaman Dang, Amit Sethi
The accuracy of deep learning methods for two foundational tasks in medical image analysis -- detection and segmentation -- can suffer from class imbalance. We propose a `switching loss' function that adaptively shifts the emphasis between foreground and background classes. While the existing loss functions to address this problem were motivated by the classification task, the switching loss is based on Dice loss, which is better suited for segmentation and detection. Furthermore, to get the most out the training samples, we adapt the loss with each mini-batch, unlike previous proposals that adapt once for the entire training set. A nucleus detector trained using the proposed loss function on a source dataset outperformed those trained using cross-entropy, Dice, or focal losses. Remarkably, without retraining on target datasets, our pre-trained nucleus detector also outperformed existing nucleus detectors that were trained on at least some of the images from the target datasets. To establish a broad utility of the proposed loss, we also confirmed that it led to more accurate ventricle segmentation in MRI as compared to the other loss functions. Our GPU-enabled pre-trained nucleus detection software is also ready to process whole slide images right out-of-the-box and is usably fast.
Structure Embedded Nucleus Classification for Histopathology Images
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong at Shenzhen（香港中文大学（深圳））；Pazhou Lab（琶洲实验室）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Artificial Intelligence Thrust, The Hong Kong University of Science and Technology at Guangzhou（香港科技大学广州人工智能部）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Guangdong Provincial Key Laboratory of Big Data Computing（广东省大数据计算重点实验室）；Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases（广东省结直肠和盆底疾病重点实验室）
University of Houston（休斯顿大学）；University Hospital of Cologne（科隆大学医院）；Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；University of Turin（都灵大学）
Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；University of Veterinary Medicine Vienna（维也纳兽医大学）；Freie Universität Berlin（柏林自由大学）；The Schwarzmann Animal Medical Center（施瓦茨曼动物医疗中心）；Michigan State University（密歇根州立大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）
NuNext: Reframing Nucleus Detection as Next-Point Detection
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Nanjing University（南京大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Science and Technology Beijing（北京科技大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
University Medicine Göttingen, Department of General, Visceral and Pediatric Surgery（乌尔姆医学院，普通外科、 visceral 外科和儿童外科部门）；Georg-August-University Göttingen, Institute of Computer Science（哥廷根大学，计算机科学学院）；CAIMed - Lower Saxony Center for AI & Causal Methods in Medicine, Göttingen（下萨克森AI与医学因果方法研究中心，哥廷根）；Cluster of Excellence Multiscale Bioimaging (MBExC), Georg-August-University Göttingen（多尺度生物成像集群（MBExC），哥廷根大学）
DeNuC: Decoupling Nuclei Detection and Classification in Histopathology
School of Automation and Electrical Engineering, University of Science and Technology Beijing（自动化与电气工程学院，北京科技大学）；University of Science and Technology Beijing（北京科技大学）；Beijing Engineering Research Center of Industrial Spectrum Imaging（工业光谱成像工程研究中心）
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）
Immunocto: a massive immune cell database auto-generated for histopathology
University College London（伦敦大学学院）；The Institute of Cancer Research（癌症研究所）；University of Birmingham（伯明翰大学）；University College London Hospitals（伦敦大学学院医院）
