AIMC Topic: Carcinoma in Situ

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Breast cancer histopathology image classification through assembling multiple compact CNNs.

BMC medical informatics and decision making Oct 22, 2019
BACKGROUND: Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnosti...
Humans Neural Networks, Computer Algorithms Computer Simulation Female Sensitivity and Specificity Breast Neoplasms Image Processing, Computer-Assisted Neoplasm Invasiveness Breast Datasets as Topic Data Compression Carcinoma in Situ
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Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Journal of the American College of Radiology : JACR Feb 2, 2018
PURPOSE: The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) o...
Humans Neural Networks, Computer Prognosis Retrospective Studies Female Aged Adult Breast Neoplasms Middle Aged Deep Learning Mammography Neoplasm Staging Carcinoma, Intraductal, Noninfiltrating Monte Carlo Method Biopsy, Large-Core Needle Carcinoma in Situ
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Single-cell analyses unravel ecosystem dynamics and intercellular crosstalk during gallbladder cancer malignant transformation.

Hepatology communications Jun 1, 2025
BACKGROUND: Gallbladder cancer (GBC) is a rare but aggressive malignancy, often detected late due to early asymptomatic stages. Understanding cellular and molecular changes from normal tissue to high-grade intraepithelial neoplasia (HGIN) and invasiv...
Humans Prognosis Female Tumor Microenvironment Male Single-Cell Analysis Epithelial Cells Cell Communication Cell Transformation, Neoplastic Gallbladder Neoplasms Carcinoma in Situ
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Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

The journal of pathology. Clinical research Nov 1, 2024
In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinom...
Humans Female Reproducibility of Results Deep Learning Image Interpretation, Computer-Assisted Observer Variation Cystadenocarcinoma, Serous Carcinoma in Situ Fallopian Tube Neoplasms
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