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
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 …
[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 …
Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
The histological analysis of tissue samples, widely used for disease diagnosis, involves
lengthy and laborious tissue preparation. Here, we show that a convolutional neural network …
lengthy and laborious tissue preparation. Here, we show that a convolutional neural network …
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue
slides that exhibit similar but not identical color appearance. Due to this color shift between …
slides that exhibit similar but not identical color appearance. Due to this color shift between …
[HTML][HTML] Machine learning methods for histopathological image analysis
D Komura, S Ishikawa - Computational and structural biotechnology journal, 2018 - Elsevier
Abundant accumulation of digital histopathological images has led to the increased demand
for their analysis, such as computer-aided diagnosis using machine learning techniques …
for their analysis, such as computer-aided diagnosis using machine learning techniques …
GANs for medical image analysis
Generative adversarial networks (GANs) and their extensions have carved open many
exciting ways to tackle well known and challenging medical image analysis problems such …
exciting ways to tackle well known and challenging medical image analysis problems such …
Segmentation of nuclei in histopathology images by deep regression of the distance map
The advent of digital pathology provides us with the challenging opportunity to automatically
analyze whole slides of diseased tissue in order to derive quantitative profiles that can be …
analyze whole slides of diseased tissue in order to derive quantitative profiles that can be …
Bci: Breast cancer immunohistochemical image generation through pyramid pix2pix
The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential
to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is …
to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is …