AIMC Topic: Breast Neoplasms

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Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer.

The American journal of surgical pathology
Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology ...

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.

Journal of digital imaging
The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI dataset. An institutional review board (IRB)-approved retrospective review of our database from 1/201...

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.

Journal of digital imaging
Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reportin...

Cell Classification in ER-Stained Whole Slide Breast Cancer Images Using Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Hormone receptor status in breast carcinoma is determined primarily to identify patients who may benefit from hormonal therapy. Estrogen receptor (ER) is one of the hormone receptor positive factors which have been recognized as a marker for which wo...

Classifying tumors by supervised network propagation.

Bioinformatics (Oxford, England)
MOTIVATION: Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion ...

Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.

Medicine and science in sports and exercise
PURPOSE: This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data.

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different...

Unsupervised multiple kernel learning for heterogeneous data integration.

Bioinformatics (Oxford, England)
MOTIVATION: Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of appli...