AIMC Topic: Breast Neoplasms

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Ultrasonographic morphological characteristics determined using a deep learning-based computer-aided diagnostic system of breast cancer.

Medicine
To investigate the correlations between ultrasonographic morphological characteristics quantitatively assessed using a deep learning-based computer-aided diagnostic system (DL-CAD) and histopathologic features of breast cancer.This retrospective stud...

A functional module states framework reveals transcriptional states for drug and target prediction.

Cell reports
Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module s...

HER2-ResNet: A HER2 classification method based on deep residual network.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: HER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target for tumor targeted therapy drug selection. Therefore, the correct detection and evaluation of HER2 ...

Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effectiv...

Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis.

Technology in cancer research & treatment
To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Digital mammograms were collected from Bahcesehir Mammographic Screening Progr...

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.

Yonsei medical journal
PURPOSE: In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture.

Hubness weighted SVM ensemble for prediction of breast cancer subtypes.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Ca...

Mathematical and deep learning analysis based on tissue dielectric properties at low frequencies predict outcome in human breast cancer.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical ...

Prediction of seroma after total mastectomy using an artificial neural network algorithm.

Breast disease
Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical records of total mastectomy patients were retrospectively reviewed. Data consisting of 120 subjects...