AIMC Topic: Precancerous Conditions
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Deep-learning based detection of gastric precancerous conditions.
Gut
Aug 2, 2019
Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.
Medical image analysis
Jul 10, 2019
Classification of benign-malignant lung nodules on chest CT is the most critical step in the early detection of lung cancer and prolongation of patient survival. Despite their success in image classification, deep convolutional neural networks (DCNNs...
Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.
Nature biomedical engineering
Oct 10, 2018
The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learni...
Determination of mammographic breast density using a deep convolutional neural network.
The British journal of radiology
Oct 1, 2018
OBJECTIVE: High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue a...
High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision.
Radiology
Oct 17, 2017
Purpose To develop a machine learning model that allows high-risk breast lesions (HRLs) diagnosed with image-guided needle biopsy that require surgical excision to be distinguished from HRLs that are at low risk for upgrade to cancer at surgery and t...
Evaluation of vermillion border descriptors and relevance vector machines discrimination model for making probabilistic predictions of solar cheilosis on digital lip photographs.
Computers in biology and medicine
Apr 25, 2015
INTRODUCTION: Solar cheilosis (SC), a common precancer of the lower lip with a high potential to progress to invasive squamous cell carcinoma, presents with characteristic morphological vermillion-skin border alterations, like the border retraction.
Raman spectroscopy in tandem with machine learning - based decision logic methods for characterization and detection of primary precancerous and cancerous cells.
The Analyst
Jul 21, 2025
Early cancer detection improves patient outcomes, but most Raman spectroscopy research has focused on discriminating between normal and malignant cells, ignoring the essential precancerous stage. This study fills that gap by combining Raman spectrosc...
FTIR-based machine learning for prediction of malignant transformation in oral epithelial dysplasia.
The Analyst
Jun 23, 2025
Oral squamous cell carcinoma (OSCC) is an aggressive cancer with a poor prognosis. Oral epithelial dysplasia (OED) is a precancerous lesion associated with an increased risk of malignant transformation (MT) into OSCC. However, current histopathologic...
Detection of precancerous lesions in cervical images of perimenopausal women using U-net deep learning.
African journal of reproductive health
Apr 23, 2025
Due to physiological changes during the perimenopausal period, the morphology of cervical cells undergoes certain alterations. Accurate cell image segmentation and lesion identification are of great significance for the early detection of precancerou...
Beyond Dysplasia: Uncovering Structure in Oral Potentially Malignant Diseases with Unsupervised Contrastive Learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Automated cancer diagnosis research often focuses on a binary task - recognize dysplasia and cancer from other lesions. However, other clinical conditions have estimated malignant transformation rates. Grouping these oral potentially malignant diseas...
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