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 …
A survey on deep learning and its applications
Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …
Convolutional neural networks in medical image understanding: a survey
DR Sarvamangala, RV Kulkarni - Evolutionary intelligence, 2022 - Springer
Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
Unbiased spatial proteomics with single-cell resolution in tissues
Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify
the entire complement of proteins in cells or tissues. Here, we review challenges and recent …
the entire complement of proteins in cells or tissues. Here, we review challenges and recent …
Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy
Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast
cancer in mammography screening practice. Design Systematic review of test accuracy …
cancer in mammography screening practice. Design Systematic review of test accuracy …
Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer
This paper investigates a deep learning method in image classification for the detection of
colorectal cancer with ResNet architecture. The exceptional performance of a deep learning …
colorectal cancer with ResNet architecture. The exceptional performance of a deep learning …
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing
Cancer accounts for a huge mortality rate due to its aggressiveness, colossal potential of
metastasis, and heterogeneity (causing resistance against chemotherapy). Lung and colon …
metastasis, and heterogeneity (causing resistance against chemotherapy). Lung and colon …
[HTML][HTML] Self supervised contrastive learning for digital histopathology
O Ciga, T Xu, AL Martel - Machine Learning with Applications, 2022 - Elsevier
Unsupervised learning has been a long-standing goal of machine learning and is especially
important for medical image analysis, where the learning can compensate for the scarcity of …
important for medical image analysis, where the learning can compensate for the scarcity of …
A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework
The field of Medicine and Healthcare has attained revolutionary advancements in the last
forty years. Within this period, the actual reasons behind numerous diseases were unveiled …
forty years. Within this period, the actual reasons behind numerous diseases were unveiled …