AIMC Topic: Breast Neoplasms

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Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Semi-automatic measurements are performed on FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-au...
Neoplasm Metastasis Deep Learning Positron Emission Tomography Computed Tomography Humans Brain Breast Neoplasms Algorithms
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Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its quantitative analysis is complicated by the number and location heterogeneity of metastatic lesions. Considering that bones are the most common locati...
Humans Prospective Studies Deep Learning Tomography, X-Ray Computed Positron Emission Tomography Computed Tomography Breast Neoplasms Fluorodeoxyglucose F18
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Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images.

Applied optics Jun 10, 2020
Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the disease is essential to increase the chances of treatment and cure of patients. Infrared thermography has emerged as a promising technique for diagnosis ...
Infrared Rays Humans Early Detection of Cancer Neural Networks, Computer Mammography Breast Neoplasms Databases, Factual Diagnosis, Computer-Assisted Female Machine Learning
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eBreCaP: extreme learning-based model for breast cancer survival prediction.

IET systems biology Jun 1, 2020
Breast cancer is the second leading cause of death in the world. Breast cancer research is focused towards its early prediction, diagnosis, and prognosis. Breast cancer can be predicted on omics profiles, clinical tests, and pathological images. The ...
DNA Methylation Genomics Computational Biology Humans Breast Neoplasms Gene Expression Profiling Machine Learning
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The Emergence of Robotic-assisted Breast Surgery: Proceed With Caution.

Annals of surgery Jun 1, 2020
Humans Breast Neoplasms Robotic Surgical Procedures Mastectomy Consensus Robotics
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Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight.

Journal of digital imaging Jun 1, 2020
Automatic multi-classification of breast cancer histopathological images has remained one of the top-priority research areas in the field of biomedical informatics, due to the great clinical significance of multi-classification in providing diagnosis...
Humans Deep Learning Female Breast Neoplasms Neural Networks, Computer Machine Learning
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Deep-Learning-Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data.

JCO clinical cancer informatics May 1, 2020
PURPOSE: Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, ...
Humans Prognosis Deep Learning Female Lymphocytes, Tumor-Infiltrating Breast Neoplasms Receptor, ErbB-2
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[Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation Apr 8, 2020
OBJECTIVE: Feature extraction of breast tumors is very important in the breast tumor detection (benign and malignant) in ultrasound image. The traditional quantitative description of breast tumors has some shortcomings, such as inaccuracy. A simple a...
Humans Algorithms Support Vector Machine Breast Neoplasms Ultrasonography
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Deep learning takes on tumours.

Nature Apr 1, 2020
Drug Evaluation, Preclinical Datasets as Topic Cell Nucleus Deep Learning Lung Neoplasms Breast Neoplasms Neoplasms Humans Software
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Computer-assisted Diagnosis of Breast Cancer by Cell Network Matrix Extraction and Multilayer Perceptron Analysis.

Annals of clinical and laboratory science Mar 1, 2020
OBJECTIVE: Diagnosis of breast cancer is based on identification of various morphologic features by histopathologic examination of the specimen. Ancillary immunohistochemical and molecular analyses provide additional information that is prognostic an...
Adenofibroma Female Computer Simulation Carcinoma, Ductal, Breast ROC Curve Humans Breast Neoplasms Prognosis Sclerosis Image Processing, Computer-Assisted Neural Networks, Computer Diagnosis, Computer-Assisted
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