Colon Cancer

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article pre...

Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.

Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second, each input variable's contribution to the prediction is usually difficult to interpret, owing to multiple nonlinear operations. Third, genetic data ...

Calculation of immune cell proportion from batch tumor gene expression profile based on support vector regression.

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune...

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which ...

Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.

METHODS: We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy. The convolutional neural network model...

Colorectal Cancer: Microsatellite Instability/Mismatch Repair Testing in the Era of Digital Pathology.

European Society of Coloproctology Colorectal Robotic Surgery Training for the Trainers Course - the first pilot experience.

AIM: Currently, there is no established colorectal specific robotic surgery Train the Trainer (TTT) course. The aim was to develop and evaluate such a...

Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images.

Pathologists are responsible for cancer type diagnoses from histopathological cancer tissues. However, it is known that microscopic examination is ted...

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review.

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold sta...

A Transparent and Adaptable Method to Extract Colonoscopy and Pathology Data Using Natural Language Processing.

Key variables recorded as text in colonoscopy and pathology reports have been extracted using natural language processing (NLP) tools that were not ea...

A comparative study on polyp classification using convolutional neural networks.

Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on t...

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

BACKGROUND: Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. T...

An automated detection system for colonoscopy images using a dual encoder-decoder model.

Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they...

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the current study, we aimed to predict the genetic infor...

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.

OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underl...

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcino...

Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study.

Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two o...

A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy.

BACKGROUND: This study aimed to establish and validate a machine learning-based model for the prediction of early phase postoperative hypertension (EP...

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised d...

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wr...