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 review on deep learning in medical image analysis
S Suganyadevi, V Seethalakshmi… - International Journal of …, 2022 - Springer
Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many
death cases and affected all sectors of human life. With gradual progression of time, COVID …
death cases and affected all sectors of human life. With gradual progression of time, COVID …
Structural crack detection using deep convolutional neural networks
Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad
range of computer vision problems. It has achieved encouraging results in numerous …
range of computer vision problems. It has achieved encouraging results in numerous …
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
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 …
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
EH Houssein, MM Emam, AA Ali… - Expert Systems with …, 2021 - Elsevier
Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …
can help decrease breast cancer mortality rates. Computer-aided detection allows …
Learning from disagreement: A survey
Abstract Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer
evidence that humans disagree, from objective tasks such as part-of-speech tagging to more …
evidence that humans disagree, from objective tasks such as part-of-speech tagging to more …
Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
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 …