AIMC Topic: Colon

Clear Filters Showing 81 to 90 of 118 articles

ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning.

Scientific reports
Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely a...

An Integrated Sensor-Model Approach for Haptic Feedback of Flexible Endoscopic Robots.

Annals of biomedical engineering
Haptic feedback for flexible endoscopic surgical robots is challenging due to space constraints for sensors and shape-dependent force hysteresis of tendon-sheath mechanisms (TSMs). This paper proposes (1) a single-axis fiber Bragg grating (FBG)-based...

The Pelvis-First Approach for Robotic Proctectomy in Patients with Redundant Abdominal Colon.

Annals of surgical oncology
BACKGROUND: Robotic surgery is increasingly performed for low rectal cancer.1 A redundant sigmoid colon makes retraction and pelvic dissection challenging. We present a 'pelvis-first' approach to robotic proctectomy where pelvic dissection occurs pri...

Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.

Computer methods and programs in biomedicine
OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative ...

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy.

Medical image analysis
In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example...

Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection.

IEEE transactions on medical imaging
Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduri...

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

PLoS medicine
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers....

Association of time to colonoscopy after a positive fecal test result and fecal hemoglobin concentration with risk of advanced colorectal neoplasia.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: We evaluated the risk of advanced colorectal neoplasia (ACRN) and colorectal cancer (CRC) according to time to colonoscopy after positive fecal immunochemical test (FIT), fecal hemoglobin concentration, and combination of both.

Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering.

IEEE transactions on medical imaging
Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease states can be directly assessed by analyzing the mid-IR spectra of...

Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images.

IEEE transactions on medical imaging
Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviati...