AIMC Topic: Early Detection of Cancer

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Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates.

Digestive diseases and sciences
BACKGROUND: ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical databases. Natural language processing (NLP) is a method used to extract meaning from unstructured o...

Detecting and classifying lesions in mammograms with Deep Learning.

Scientific reports
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimat...

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

Physics in medicine and biology
Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive pr...

Acral melanoma detection using a convolutional neural network for dermoscopy images.

PloS one
BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the h...

Robotic versus laparoscopic radical hysterectomy in early cervical cancer: A case matched control study.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: This study aims at evaluating the feasibility, surgical outcome and oncological results observed after robotic radical hysterectomy (RH) compared to laparoscopy for patients with early stage cervical cancer (ECC) patients.

Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study.

Scientific reports
We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digita...

An expert system design to diagnose cancer by using a new method reduced rule base.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: A Medical Expert System (MES) was developed which uses Reduced Rule Base to diagnose cancer risk according to the symptoms in an individual. A total of 13 symptoms were used. With the new MES, the reduced rules are controll...

Learning-based classification of informative laryngoscopic frames.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the co...

Deep Convolutional Neural Networks for breast cancer screening.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health c...