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 …
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …
development. However, advanced materials can take decades to discover and decades …
Study on the prediction of stock price based on the associated network model of LSTM
G Ding, L Qin - International Journal of Machine Learning and …, 2020 - Springer
Stock market has received widespread attention from investors. It has always been a hot
spot for investors and investment companies to grasp the change regularity of the stock …
spot for investors and investment companies to grasp the change regularity of the stock …
Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction
Shortage of fully annotated datasets has been a limiting factor in developing deep learning
based image segmentation algorithms and the problem becomes more pronounced in multi …
based image segmentation algorithms and the problem becomes more pronounced in multi …
Micro-Net: A unified model for segmentation of various objects in microscopy images
Object segmentation and structure localization are important steps in automated image
analysis pipelines for microscopy images. We present a convolution neural network (CNN) …
analysis pipelines for microscopy images. We present a convolution neural network (CNN) …
Quantitative digital microscopy with deep learning
Video microscopy has a long history of providing insight and breakthroughs for a broad
range of disciplines, from physics to biology. Image analysis to extract quantitative …
range of disciplines, from physics to biology. Image analysis to extract quantitative …
Mdu-net: Multi-scale densely connected u-net for biomedical image segmentation
Biomedical image segmentation plays a central role in quantitative analysis, clinical
diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) …
diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) …
A review of nuclei detection and segmentation on microscopy images using deep learning with applications to unbiased stereology counting
The detection and segmentation of stained cells and nuclei are essential prerequisites for
subsequent quantitative research for many diseases. Recently, deep learning has shown …
subsequent quantitative research for many diseases. Recently, deep learning has shown …
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 …
SCAU-net: spatial-channel attention U-net for gland segmentation
With the development of medical technology, image semantic segmentation is of great
significance for morphological analysis, quantification, and diagnosis of human tissues …
significance for morphological analysis, quantification, and diagnosis of human tissues …