AIMC Topic: Breast Neoplasms

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Analyzing interactions on combining multiple clinical guidelines.

Artificial intelligence in medicine
Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, importa...

Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images.

IEEE journal of biomedical and health informatics
Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neu...

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

IEEE/ACM transactions on computational biology and bioinformatics
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown t...

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.

Scientific reports
Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligen...

Node-based differential network analysis in genomics.

Computational biology and chemistry
Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume...

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer.

Computational and mathematical methods in medicine
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. A...

Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome.

Artificial intelligence in medicine
Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue specificities of diseases. Moreover, disease genes, drug targets and protein interaction (PPI) networks r...

MR-based synthetic CT generation using a deep convolutional neural network method.

Medical physics
PURPOSE: Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiothera...

MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images.

Journal of microscopy
Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the r...

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks.

Journal of biomedical informatics
Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we prov...