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A state-of-the-art survey of artificial neural networks for whole-slide image analysis: from popular convolutional neural networks to potential visual transformers
In recent years, with the advancement of computer-aided diagnosis (CAD) technology and
whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the …
whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the …
A comprehensive survey of intestine histopathological image analysis using machine vision approaches
Y **g, C Li, T Du, T Jiang, H Sun, J Yang, L Shi… - Computers in Biology …, 2023 - Elsevier
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is
the third most common malignancy and the fourth leading cause of cancer death worldwide …
the third most common malignancy and the fourth leading cause of cancer death worldwide …
A state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification
Microscopic image analysis technology helps solve the inadvertences of artificial traditional
methods in disease, wastewater treatment, and environmental change monitoring analysis …
methods in disease, wastewater treatment, and environmental change monitoring analysis …
[HTML][HTML] Few-shot learning based histopathological image classification of colorectal cancer
R Li, X Li, H Sun, J Yang, M Rahaman, M Grzegozek… - Intelligent …, 2024 - Elsevier
Background Colorectal cancer is a prevalent and deadly disease worldwide, posing
significant diagnostic challenges. Traditional histopathologic image classification is often …
significant diagnostic challenges. Traditional histopathologic image classification is often …
Exploring feasibility of citric acid infused lignocellulosic waste derived from chestnut and water melon peels for phytofiltration of Eosin yellow dye from water
The adsorption efficiency of cheap, ecofriendly, and easily available agro-waste, Trapa
natans (Chestnut) and Citrullus lanatus (Watermelon) peels, has been investigated in their …
natans (Chestnut) and Citrullus lanatus (Watermelon) peels, has been investigated in their …
[HTML][HTML] Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal
Histological analysis with microscopy is the gold standard to diagnose and stage cancer,
where slides or whole slide images are analyzed for cell morphological and spatial features …
where slides or whole slide images are analyzed for cell morphological and spatial features …
AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoising, semantic segmentation, and radiomics evaluation
Background and objective: The metabolic syndrome induced by obesity is closely
associated with cardiovascular disease, and the prevalence is increasing globally, year by …
associated with cardiovascular disease, and the prevalence is increasing globally, year by …
A high-level feature channel attention UNet network for cholangiocarcinoma segmentation from microscopy hyperspectral images
H Gao, M Yang, X Cao, Q Liu, P Xu - Machine Vision and Applications, 2023 - Springer
Pathological diagnosis is the gold standard for the diagnosis of cholangiocarcinoma. The
manual segmentation of pathology sections is time-consuming. Automatic segmentation has …
manual segmentation of pathology sections is time-consuming. Automatic segmentation has …
[HTML][HTML] Computer-aided colorectal cancer diagnosis: AI-driven image segmentation and classification
ÇB Erdaş - PeerJ Computer Science, 2024 - peerj.com
Colorectal cancer is an enormous health concern since it is among the most lethal types of
malignancy. The manual examination has its limitations, including subjectivity and data …
malignancy. The manual examination has its limitations, including subjectivity and data …
To pretrain or not to pretrain? a case study of domain-specific pretraining for semantic segmentation in histopathology
Annotating medical imaging datasets is costly, so fine-tuning (or transfer learning) is the
most effective method for digital pathology vision applications such as disease classification …
most effective method for digital pathology vision applications such as disease classification …