AIMC Topic: Breast

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DCANet: Dual contextual affinity network for mass segmentation in whole mammograms.

Medical physics
PURPOSE: Breast mass segmentation in mammograms remains a crucial yet challenging topic in computer-aided diagnosis systems. Existing algorithms mainly used mass-centered patches to achieve mass segmentation, which is time-consuming and unstable in c...

Investigation of clinical target volume segmentation for whole breast irradiation using three-dimensional convolutional neural networks with gradient-weighted class activation mapping.

Radiological physics and technology
This study aims to implement three-dimensional convolutional neural networks (3D-CNN) for clinical target volume (CTV) segmentation for whole breast irradiation and investigate the focus of 3D-CNNs during decision-making using gradient-weighted class...

Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification.

Deep Feature Representations for Variable-Sized Regions of Interest in Breast Histopathology.

IEEE journal of biomedical and health informatics
OBJECTIVE: Modeling variable-sized regions of interest (ROIs) in whole slide images using deep convolutional networks is a challenging task, as these networks typically require fixed-sized inputs that should contain sufficient structural and contextu...

Multi-View Mammographic Density Classification by Dilated and Attention-Guided Residual Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Breast density is widely adopted to reflect the likelihood of early breast cancer development. Existing methods of mammographic density classification either require steps of manual operations or achieve only moderate classification accuracy due to t...

Radiomics and deep learning methods in expanding the use of screening breast MRI.

European radiology
• The use of screening breast MRI is expanding beyond high-risk women to include intermediate- and average-risk women.• The study by Pötsch et al uses a radiomics-based method to decrease the number of benign biopsies while maintaining high sensitivi...

A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.

Scientific reports
Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in ...

Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast dosimetry in mammography is an important aspect of radioprotection since women are exposed periodically to ionizing radiation due to breast cancer screening programs. Mean glandular dose (MGD) is the standard quantity employed for the...

Domain adaptation and self-supervised learning for surgical margin detection.

International journal of computer assisted radiology and surgery
PURPOSE: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in s...