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 …
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 …
Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods
HG Nguyen, A Blank, HE Dawson, A Lugli, I Zlobec - Scientific reports, 2021 - nature.com
Tissue microarray (TMA) core images are a treasure trove for artificial intelligence
applications. However, a common problem of TMAs is multiple sectioning, which can …
applications. However, a common problem of TMAs is multiple sectioning, which can …
Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning
K Sirinukunwattana, E Domingo, SD Richman… - Gut, 2021 - gut.bmj.com
Objective Complex phenotypes captured on histological slides represent the biological
processes at play in individual cancers, but the link to underlying molecular classification …
processes at play in individual cancers, but the link to underlying molecular classification …
An effective deep learning architecture combination for tissue microarray spots classification of h&e stained colorectal images
HG Nguyen, A Blank, A Lugli… - 2020 IEEE 17th …, 2020 - ieeexplore.ieee.org
Tissue microarray (TMA) assessment of histomorphological biomarkers contributes to more
accurate prediction of outcome of patients with colorectal cancer (CRC), a common disease …
accurate prediction of outcome of patients with colorectal cancer (CRC), a common disease …
Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review
Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …
common in men, with an increasing incidence. Pathology diagnosis complemented with …
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 …
Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical …
Y Tan, R Liu, J Xue, Z Feng - Cancer Medicine, 2024 - Wiley Online Library
Objective This retrospective observational study aims to develop and validate artificial
intelligence (AI) pathomics models based on pathological Hematoxylin–Eosin (HE) slides …
intelligence (AI) pathomics models based on pathological Hematoxylin–Eosin (HE) slides …
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 …
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