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 …
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 …
Weakly supervised deep learning for whole slide lung cancer image analysis
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …
[HTML][HTML] Methods for segmentation and classification of digital microscopy tissue images
High-resolution microscopy images of tissue specimens provide detailed information about
the morphology of normal and diseased tissue. Image analysis of tissue morphology can …
the morphology of normal and diseased tissue. Image analysis of tissue morphology can …
Context-aware convolutional neural network for grading of colorectal cancer histology images
Digital histology images are amenable to the application of convolutional neural networks
(CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally …
(CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally …
Deep multi-magnification networks for multi-class breast cancer image segmentation
Pathologic analysis of surgical excision specimens for breast carcinoma is important to
evaluate the completeness of surgical excision and has implications for future treatment …
evaluate the completeness of surgical excision and has implications for future treatment …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Learning where to see: a novel attention model for automated immunohistochemical scoring
T Qaiser, NM Rajpoot - IEEE transactions on medical imaging, 2019 - ieeexplore.ieee.org
Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on
invasive breast cancer is regarded as a significant predictive and prognostic marker. We …
invasive breast cancer is regarded as a significant predictive and prognostic marker. We …
Context-aware learning using transferable features for classification of breast cancer histology images
Convolutional neural networks (CNNs) have been recently used for a variety of histology
image analysis. However, availability of a large dataset is a major prerequisite for training a …
image analysis. However, availability of a large dataset is a major prerequisite for training a …
Predicting cancer with a recurrent visual attention model for histopathology images
A BenTaieb, G Hamarneh - … , Granada, Spain, September 16-20, 2018 …, 2018 - Springer
Automatically recognizing cancers from multi-gigapixel whole slide histopathology images is
one of the challenges facing machine and deep learning based solutions for digital …
one of the challenges facing machine and deep learning based solutions for digital …