Colon Cancer

DAN-PD: Domain adaptive network with parallel decoder for polyp segmentation.

Endoscopy is essential for polyp diagnosis and prevention of colorectal cancer. Many deep learning methods have been proposed to perform automatic semantic segmentation of polyps in endoscopic images. However, labeled training images are always scarce, and the styles of endoscopic images from different medical centers vary greatly. The annotation of medical images requires much effort, and how to ...

Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.

Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques can be used to reduce the workload of experts and to produce quick solutions. The nuclei found in the histopathology dataset are an essential parameter in disease detection. The nucleus segmentation was performed using the colorectal histology MNIST...

Deep learning-based prediction of treatment prognosis from nasal polyp histology slides.

BACKGROUND: Histopathology of nasal polyps contains rich prognostic information, which is difficult to extract objectively. In the present study, we a...

A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.

AIMS: The reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularit...

Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability.

BACKGROUND AND AIMS: Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) researc...

Acute colonic flexures: the basis for developing an artificial intelligence-based tool for predicting the course of colonoscopy.

Tortuosity of the colon is an important parameter for predicting the course of colonoscopy. Computed tomography scans of the abdominal cavity were per...

Lightweight Deep Learning Classification Model for Identifying Low-Resolution CT Images of Lung Cancer.

With an astounding five million fatal cases every year, lung cancer is among the leading causes of mortality worldwide for both men and women. The dia...

Preoperative data-based deep learning model for predicting postoperative survival in pancreatic cancer patients.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis even after curative resection. A deep learning-based stratification of postop...

PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images.

BACKGROUND AND OBJECTIVE: Recent studies have shown that colorectal cancer (CRC) patients with microsatellite instability high (MSI-H) are more likely...

UC-NfNet: Deep learning-enabled assessment of ulcerative colitis from colonoscopy images.

Ulcerative colitis (UC) belongs to the inflammatory bowel disease (IBD) family, which is mainly caused by inflammation of the tissue in the colon and ...

Polyp segmentation with consistency training and continuous update of pseudo-label.

Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled da...

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiaut...

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning.

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an impor...

MRI-Based Artificial Intelligence in Rectal Cancer.

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old....

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma.

BACKGROUND: Cuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinom...

Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation.

BACKGROUND: Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intell...

Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.

PURPOSE: To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learnin...

Anti-Colorectal Cancer Effects of (Bull.: Fr.) P. Karst. Spore Powder through Regulation of Gut Microbiota-Mediated JAK/STAT Signaling.

(Bull.: Fr.) P. Karst. spore powder (IHS) contains polyphenols and triterpenoids with pharmacological effects. Here, we analyzed its composition, and...

A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study.

BACKGROUND: The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and w...

Artificial Intelligence for Colonoscopy: Past, Present, and Future.

During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising met...