[HTML][HTML] Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions

F Prezja, S Äyrämö, I Pölönen, T Ojala, S Lahtinen… - Scientific Reports, 2023 - nature.com
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 …

Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning

F Prezja, L Annala, S Kiiskinen, S Lahtinen… - arXiv preprint arXiv …, 2023 - arxiv.org
In routine colorectal cancer management, histologic samples stained with hematoxylin and
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

JN Kather, J Krisam, P Charoentong, T Luedde… - PLoS …, 2019 - journals.plos.org
Background For virtually every patient with colorectal cancer (CRC), hematoxylin–eosin
(HE)–stained tissue slides are available. These images contain quantitative information …

[HTML][HTML] Deep learning based tissue analysis predicts outcome in colorectal cancer

D Bychkov, N Linder, R Turkki, S Nordling… - Scientific reports, 2018 - nature.com
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 …

Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?

DD Chlorogiannis, GI Verras, V Tzelepi… - Gastroenterology …, 2023 - termedia.pl
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 …

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

D Bychkov, R Turkki, C Haglund… - Medical Imaging …, 2016 - spiedigitallibrary.org
Recent advances in computer vision enable increasingly accurate automated pattern
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 …

[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 …

[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 …

Deep learning for colon cancer histopathological images analysis

AB Hamida, M Devanne, J Weber, C Truntzer… - Computers in Biology …, 2021 - Elsevier
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 …