AIMC Topic: Colorectal Neoplasms

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Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images.

Scientific reports
Colorectal cancer (CRC) is the second popular cancer in females and third in males, with an increased number of cases. Pathology diagnoses complemented with predictive and prognostic biomarker information is the first step for personalized treatment....

IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis.

IEEE transactions on medical imaging
Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal disease diagnosis is crucial for assessing lesion evolution and determining treatment plans. Howeve...

Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.

Endoscopy
BACKGROUND:  Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the div...

Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the immunotherapy response in endometrial cancer (EC) and colorectal cancer (CRC). Tumor mutational burden ...

A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study.

JMIR cancer
BACKGROUND: The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily li...

Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer.

Biochimica et biophysica acta. Molecular basis of disease
Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. However, effectively targeting DTP cells remains challenging. We used single-cell RNA sequencing (sc...

Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.

Scientific reports
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and a...

Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis.

Abdominal radiology (New York)
BACKGROUND: Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for pr...

PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention.

Medical & biological engineering & computing
In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies a...