Oncology/Hematology

Colon Cancer

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

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Single Shot Multibox Detector Automatic Polyp Detection Network Based on Gastrointestinal Endoscopic Images.

PURPOSE: In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate ...

A novel Joint-Net model for recognizing small-bowel polyp images.

INTRODUCTION: To automatically recognize polyps of enteroscopy images and avoid pathological change,...

Detection of colorectal lesions during colonoscopy.

Owing to its high mortality rate, the prevention of colorectal cancer is of particular importance. T...

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.

Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers ...

Short-term outcomes in robot-assisted compared to laparoscopic colon cancer resections: a systematic review and meta-analysis.

BACKGROUND: Robot-assisted surgery is increasingly adopted in colorectal surgery. However, evidence ...

Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps.

IMPORTANCE: Colorectal polyps are common, and their histopathologic classification is used in the pl...

Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor t...

Low-value care and excess out-of-pocket expenditure among older adults with incident cancer - A machine learning approach.

OBJECTIVE: To evaluate the association of low-value care with excess out-of-pocket expenditure among...

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, un...

BH-index: A predictive system based on serum biomarkers and ensemble learning for early colorectal cancer diagnosis in mass screening.

BACKGROUND AND OBJECTIVE: Colorectal cancer is one of the most common malignancies among the general...

A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma.

Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mech...

Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.

PURPOSE: Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal ad...

Deep learning can predict lymph node status directly from histology in colorectal cancer.

BACKGROUND: Lymph node status is a prognostic marker and strongly influences therapeutic decisions i...

CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer.

Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis of th...

Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation.

Accurate segmentation of the polyps from colonoscopy images provides useful information for the diag...

A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification.

Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology exam...

3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT images.

The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy opt...

Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art.

Cancer early detection increases the chances of survival. Some cancer types, like pancreatic cancer,...

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We ...

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