[HTML][HTML] Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions
Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer
patients. These slides are often not used to define objective biomarkers for patient …
patients. These slides are often not used to define objective biomarkers for patient …
Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning
In routine colorectal cancer management, histologic samples stained with hematoxylin and
eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for …
eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for …
[HTML][HTML] Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Background For virtually every patient with colorectal cancer (CRC), hematoxylin–eosin
(HE)–stained tissue slides are available. These images contain quantitative information …
(HE)–stained tissue slides are available. These images contain quantitative information …
[HTML][HTML] Deep learning based tissue analysis predicts outcome in colorectal cancer
Image-based machine learning and deep learning in particular has recently shown expert-
level accuracy in medical image classification. In this study, we combine convolutional and …
level accuracy in medical image classification. In this study, we combine convolutional and …
Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?
Colorectal cancer is one of the most prevalent types of cancer, with histopathologic
examination of biopsied tissue samples remaining the gold standard for diagnosis. During …
examination of biopsied tissue samples remaining the gold standard for diagnosis. During …
Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer
Recent advances in computer vision enable increasingly accurate automated pattern
classification. In the current study we evaluate whether a convolutional neural network …
classification. In the current study we evaluate whether a convolutional neural network …
[HTML][HTML] Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework
M Khazaee Fadafen, K Rezaee - Scientific Reports, 2023 - nature.com
Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital
pathology is essential for assessing prognosis. Due to the increasing resolution and quantity …
pathology is essential for assessing prognosis. Due to the increasing resolution and quantity …
[HTML][HTML] 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 …
[HTML][HTML] Colorectal cancer detection based on deep learning
L Xu, B Walker, PI Liang, Y Tong, C Xu, YC Su… - Journal of Pathology …, 2020 - Elsevier
Introduction: The initial point in the diagnostic workup of solid tumors remains manual, with
the assessment of hematoxylin and eosin (H&E)-stained tissue sections by microscopy. This …
the assessment of hematoxylin and eosin (H&E)-stained tissue sections by microscopy. This …
Deep learning for colon cancer histopathological images analysis
Nowadays, digital pathology plays a major role in the diagnosis and prognosis of tumours.
Unfortunately, existing methods remain limited when faced with the high resolution and size …
Unfortunately, existing methods remain limited when faced with the high resolution and size …
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