[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 …
A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
Two path gland segmentation algorithm of colon pathological image based on local semantic guidance
Colonic adenocarcinoma is a disease severely endangering human life caused by mucosal
epidermal carcinogenesis. The segmentation of potentially cancerous glands is the key in …
epidermal carcinogenesis. The segmentation of potentially cancerous glands is the key in …
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation
in histology images is challenging in variable conditions (clinical wild), such as poor staining …
in histology images is challenging in variable conditions (clinical wild), such as poor staining …
A hybrid deep learning approach for gland segmentation in prostate histopathological images
M Salvi, M Bosco, L Molinaro, A Gambella… - Artificial Intelligence in …, 2021 - Elsevier
Background In digital pathology, the morphology and architecture of prostate glands have
been routinely adopted by pathologists to evaluate the presence of cancer tissue. The …
been routinely adopted by pathologists to evaluate the presence of cancer tissue. The …
Gland segmentation in colorectal cancer histopathological images using U-net inspired convolutional network
The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology
images with a wide range of histologic grades of cancer is quite challenging. The …
images with a wide range of histologic grades of cancer is quite challenging. The …
Nuclei and glands instance segmentation in histology images: a narrative review
Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
DARMF-UNet: A dual-branch attention-guided refinement network with multi-scale features fusion U-Net for gland segmentation
J Sun, X Zhang, X Li, R Liu, T Wang - Computers in Biology and Medicine, 2023 - Elsevier
Accurate gland segmentation is critical in determining adenocarcinoma. Automatic gland
segmentation methods currently suffer from challenges such as less accurate edge …
segmentation methods currently suffer from challenges such as less accurate edge …
Region segmentation of whole-slide images for analyzing histological differentiation of prostate adenocarcinoma using ensemble efficientnetb2 u-net with transfer …
K Ikromjanov, S Bhattacharjee, RI Sumon, YB Hwang… - Cancers, 2023 - mdpi.com
Simple Summary Differentiating growth patterns of the tumor glands in prostate biopsy tissue
images is a challenging task for pathologists. Therefore, advanced technology, especially …
images is a challenging task for pathologists. Therefore, advanced technology, especially …
Deep learning application for analyzing of constituents and their correlations in the interpretations of medical images
The need for time and attention, given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …