Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
Computational pathology in cancer diagnosis, prognosis, and prediction–present day and prospects
G Verghese, JK Lennerz, D Ruta, W Ng… - The Journal of …, 2023 - Wiley Online Library
Computational pathology refers to applying deep learning techniques and algorithms to
analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led …
analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led …
PAGE-Net: interpretable and integrative deep learning for survival analysis using histopathological images and genomic data
J Hao, SC Kosaraju, NZ Tsaku, DH Song… - Pacific Symposium on …, 2019 - World Scientific
The integration of multi-modal data, such as histopathological images and genomic data, is
essential for understanding cancer heterogeneity and complexity for personalized …
essential for understanding cancer heterogeneity and complexity for personalized …
Machine learning approaches for pathologic diagnosis
D Komura, S Ishikawa - Virchows Archiv, 2019 - Springer
Abstract Machine learning techniques, especially deep learning techniques such as
convolutional neural networks, have been successfully applied to general image …
convolutional neural networks, have been successfully applied to general image …
[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
TIAToolbox as an end-to-end library for advanced tissue image analytics
Background Computational pathology has seen rapid growth in recent years, driven by
advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel …
advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel …
Deep learning in histopathology: A review
Histopathology is diagnosis based on visual examination of tissue sections under a
microscope. With the growing number of digitally scanned tissue slide images, computer …
microscope. With the growing number of digitally scanned tissue slide images, computer …
Deep learning-based survival prediction for multiple cancer types using histopathology images
Providing prognostic information at the time of cancer diagnosis has important implications
for treatment and monitoring. Although cancer staging, histopathological assessment …
for treatment and monitoring. Although cancer staging, histopathological assessment …
Recent advances of deep learning for computational histopathology: principles and applications
Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …