AIMC Topic: Breast

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Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentiall...

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

Computers in biology and medicine
This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algor...

Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.

IEEE transactions on medical imaging
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodolog...

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of...

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

Computational and mathematical methods in medicine
We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and lo...

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

Medical image analysis
We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combi...

Large scale deep learning for computer aided detection of mammographic lesions.

Medical image analysis
Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a h...

Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.

PloS one
Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used to represent ...

An image-guided automated robot for MRI breast biopsy.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The IGAR (Image-guided Automated Robot) is a robotic platform capable of performing highly accurate clinical interventions under image guidance. The IGAR is unique in that it demonstrates MRI compatibility and maintains safe operation, ad...

Iterative fuzzy segmentation for an accurate delimitation of the breast region.

Computer methods and programs in biomedicine
In mammographic images, extracting different anatomical structures and tissues types is a critical requirement for the breast cancer diagnosis. For instance, separating breast and background regions increases the accuracy and efficiency of mammograph...