Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
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 …
Medical image analysis based on deep learning approach
M Puttagunta, S Ravi - Multimedia tools and applications, 2021 - Springer
Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer
C Ho, Z Zhao, XF Chen, J Sauer, SA Saraf… - Scientific reports, 2022 - nature.com
Colorectal cancer is one of the most common cancers worldwide, accounting for an annual
estimated 1.8 million incident cases. With the increasing number of colonoscopies being …
estimated 1.8 million incident cases. With the increasing number of colonoscopies being …
Deep learning for computational cytology: A survey
Computational cytology is a critical, rapid-developing, yet challenging topic in medical
image computing concerned with analyzing digitized cytology images by computer-aided …
image computing concerned with analyzing digitized cytology images by computer-aided …
Deep learning for bone marrow cell detection and classification on whole-slide images
Bone marrow (BM) examination is an essential step in both diagnosing and managing
numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of …
numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of …
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis.
However, due to the large scale of WSIs and various sizes of the abnormal area, how to …
However, due to the large scale of WSIs and various sizes of the abnormal area, how to …
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 …
Camel: A weakly supervised learning framework for histopathology image segmentation
G Xu, Z Song, Z Sun, C Ku, Z Yang… - Proceedings of the …, 2019 - openaccess.thecvf.com
Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To
automatically segment the cancerous regions, fully supervised segmentation algorithms …
automatically segment the cancerous regions, fully supervised segmentation algorithms …