AIMC Topic: Breast Neoplasms

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Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?

Journal of healthcare engineering
The sonogram is currently an effective cancer screening and diagnosis way due to the convenience and harmlessness in humans. Traditionally, lesion boundary segmentation is first adopted and then classification is conducted, to reach the judgment of b...

Patch-based system for Classification of Breast Histology images using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost...

Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.

Medical physics
PURPOSE: Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively e...

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Radiology
Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, ...

Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network.

Journal of biophotonics
The benchmark method for the evaluation of breast cancers involves microscopic testing of a hematoxylin and eosin (H&E)-stained tissue biopsy. Resurgery is required in 20% to 30% of cases because of incomplete excision of malignant tissues. Therefore...

Artificial Intelligence in Breast Imaging: Potentials and Limitations.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption.

A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification.

AJR. American journal of roentgenology
OBJECTIVE: The goal of augmented intelligence is to increase efficiency and effectiveness in practice. To achieve this, augmented intelligence technologies are being asked to perform a range of tasks, from simple to complex and quantitative. The deve...

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.

Radiology
Purpose To compare the diagnostic performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for classification of contrast agent-enhancing lesions as benign or malignant at multiparametric breast MRI. Materials an...

Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected b...

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

Magnetic resonance in medicine
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response t...