From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology

OSM El Nahhas, M van Treeck, G Wölflein, M Unger… - Nature …, 2025‏ - nature.com
Hematoxylin-and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis
of cancer. In recent years, development of deep learning-based methods in computational …

[HTML][HTML] Applications of artificial intelligence in digital pathology for gastric cancer

S Chen, P Ding, H Guo, L Meng, Q Zhao… - Frontiers in …, 2024‏ - pmc.ncbi.nlm.nih.gov
Gastric cancer is one of the most common cancers and is one of the leading causes of
cancer-related deaths in worldwide. Early diagnosis and treatment are essential for a …

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 …

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 …

Feddbl: Communication and data efficient federated deep-broad learning for histopathological tissue classification

T Deng, Y Huang, G Han, Z Shi, J Lin… - IEEE Transactions …, 2024‏ - ieeexplore.ieee.org
Histopathological tissue classification is a fundamental task in computational pathology.
Deep learning (DL)-based models have achieved superior performance but centralized …

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 …

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging

OL Saldanha, J Zhu, G Müller-Franzes… - Communications …, 2025‏ - nature.com
Background Over the next 5 years, new breast cancer screening guidelines recommending
magnetic resonance imaging (MRI) for certain patients will significantly increase the volume …

Artificial Intelligence in Gastrointestinal Cancer Research: Image Learning Advances and Applications

S Zhou, Y **e, X Feng, Y Li, L Shen, Y Chen - Cancer Letters, 2025‏ - Elsevier
With the rapid advancement of artificial intelligence (AI) technologies, including deep
learning, large language models, and neural networks, these methodologies are …

Targeting amino acid metabolism to inhibit gastric cancer progression and promote anti-tumor immunity: a review

Y Jiang, Q Tao, X Qiao, Y Yang, C Peng… - Frontiers in …, 2025‏ - frontiersin.org
The incidence of gastric cancer remains high and poses a serious threat to human health.
Recent comprehensive investigations into amino acid metabolism and immune system …

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 …