Oncology/Hematology

Colon Cancer

Latest AI and machine learning research in colon cancer for healthcare professionals.

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piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer.

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates f...

Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach.

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While con...

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.

BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging ...

Data privacy-aware machine learning approach in pancreatic cancer diagnosis.

PROBLEM: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its adv...

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major c...

Artificial Intelligence and cancer: Profile of registered clinical trials.

Artificial Intelligence (AI) has made significant strides due to advancements in processing algorith...

Enhancing metastatic colorectal cancer prediction through advanced feature selection and machine learning techniques.

BACKGROUND AND AIMS: Colorectal cancer (CRC) is the third most prevalent cancer globally, posing a s...

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.

Delayed diagnosis and treatment resistance result in high pancreatic ductal adenocarcinoma (PDAC) mo...

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and m...

Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning.

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristic...

Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Artificial intelligence (AI) is increasingly used to improve adenoma detection ...

Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model.

BACKGROUND: There is an increasing demand for automated surgical skill assessment to solve issues su...

Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy.

BACKGROUND: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique abi...

A prognostic biomarker of disulfidptosis constructed by machine learning framework model as potential reporters of pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD), known for its high lethality, has not been thoroughly ...

Polar contrast attention and skip cross-channel aggregation for efficient learning in U-Net.

The performance of existing lesion semantic segmentation models has shown a steady improvement with ...

Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer.

Optimizers are the bottleneck of the training process of any Convolutionolution neural networks (CNN...

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, ...

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