From whole-slide image to biomarker prediction: a protocol for end-to-end deep learning in computational pathology
OSM El Nahhas, M van Treeck, G Wölflein… - arXiv preprint arXiv …, 2023 - arxiv.org
Hematoxylin-and eosin (H&E) stained whole-slide images (WSIs) are the foundation of
diagnosis of cancer. In recent years, development of deep learning-based methods in …
diagnosis of cancer. In recent years, development of deep learning-based methods in …
Feddbl: Communication and data efficient federated deep-broad learning for histopathological tissue classification
Histopathological tissue classification is a fundamental task in computational pathology.
Deep learning (DL)-based models have achieved superior performance but centralized …
Deep learning (DL)-based models have achieved superior performance but centralized …
Künstliche Intelligenz und digitale Pathologie als Treiber der Präzisionsonkologie
Zusammenfassung Hintergrund Die Digitalisierung bietet viele Chancen zur Verbesserung
von Diagnostik und Therapien bei Krebserkrankungen, insbesondere auch im Bereich der …
von Diagnostik und Therapien bei Krebserkrankungen, insbesondere auch im Bereich der …
Integrating deep learning for accurate gastrointestinal cancer classification: a comprehensive analysis of MSI and MSS patterns using histopathology data
AA Wafa, RM Essa, AA Abohany… - Neural Computing and …, 2024 - Springer
Early detection of microsatellite instability (MSI) and microsatellite stability (MSS) is crucial in
the fight against gastrointestinal (GI) cancer. MSI is a sign of genetic instability often …
the fight against gastrointestinal (GI) cancer. MSI is a sign of genetic instability often …
Predicting treatment response in multicenter non-small cell lung cancer patients based on federated learning
Y Liu, J Huang, JC Chen, W Chen, Y Pan, J Qiu - BMC cancer, 2024 - Springer
Background Multicenter non-small cell lung cancer (NSCLC) patient data is information-rich.
However, its direct integration becomes exceptionally challenging due to constraints …
However, its direct integration becomes exceptionally challenging due to constraints …
Swarm Learning: A Survey of Concepts, Applications, and Trends
E Shammar, X Cui, MAA Al-qaness - arXiv preprint arXiv:2405.00556, 2024 - arxiv.org
Deep learning models have raised privacy and security concerns due to their reliance on
large datasets on central servers. As the number of Internet of Things (IoT) devices …
large datasets on central servers. As the number of Internet of Things (IoT) devices …
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification
L Qu, C Wang, Y Shi - arXiv preprint arXiv:2404.15585, 2024 - arxiv.org
The application of deep learning techniques to medical problems has garnered widespread
research interest in recent years, such as applying convolutional neural networks to medical …
research interest in recent years, such as applying convolutional neural networks to medical …
Prediction of Microsatellite Instability From Gastric Histological Images Based on Residual Attention Networks With Non-Local Modules
SN Yu, SC Huang, WC Wang, YP Chang… - IEEE …, 2023 - ieeexplore.ieee.org
Gastric cancer can be classified into different subtypes according to their genetic expression.
Microsatellite instability (MSI) is one of these subtypes and an important clinical marker for …
Microsatellite instability (MSI) is one of these subtypes and an important clinical marker for …
Swarm mutual learning
K Haiyan, W Jiakang - Complex & Intelligent Systems, 2024 - Springer
With the rapid growth of big data, extracting meaningful knowledge from data is crucial for
machine learning. The existing Swarm Learning data collaboration models face challenges …
machine learning. The existing Swarm Learning data collaboration models face challenges …
[PDF][PDF] 面向无线边缘网络的分层Stackelberg 博弈群体激励方法
康海燕， 冀珊珊 - 电子学报, 2024 - ejournal.org.cn
现有分布式机器学习模型的相关激励机制大多基于单层服务器架构, 难以适应当前异构无线计算
场景, 同时存在计算资源分配不平衡, 通信成本高昂等问题. 针对上述问题, 创新地提出一种面向 …
场景, 同时存在计算资源分配不平衡, 通信成本高昂等问题. 针对上述问题, 创新地提出一种面向 …