44 research outputs found
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain
tumor segmentation. However, it's common to have missing imaging modalities in
clinical practice. Since there exists a strong correlation between multi
modalities, a novel correlation representation block is proposed to specially
discover the latent multi-source correlation. Thanks to the obtained
correlation representation, the segmentation becomes more robust in the case of
missing modalities. The model parameter estimation module first maps the
individual representation produced by each encoder to obtain independent
parameters, then, under these parameters, the correlation expression module
transforms all the individual representations to form a latent multi-source
correlation representation. Finally, the correlation representations across
modalities are fused via the attention mechanism into a shared representation
to emphasize the most important features for segmentation. We evaluate our
model on BraTS 2018 datasets, it outperforms the current state-of-the-art
method and produces robust results when one or more modalities are missing.Comment: 9 pages, 6 figures, accepted by MICCAI 202
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks
There is a common belief that the successful training of deep neural networks
requires many annotated training samples, which are often expensive and
difficult to obtain especially in the biomedical imaging field. While it is
often easy for researchers to use data augmentation to expand the size of
training sets, constructing and generating generic augmented data that is able
to teach the network the desired invariance and robustness properties using
traditional data augmentation techniques is challenging in practice. In this
paper, we propose a novel automatic data augmentation method that uses
generative adversarial networks to learn augmentations that enable machine
learning based method to learn the available annotated samples more
efficiently. The architecture consists of a coarse-to-fine generator to capture
the manifold of the training sets and generate generic augmented data. In our
experiments, we show the efficacy of our approach on a Magnetic Resonance
Imaging (MRI) image, achieving improvements of 3.5% Dice coefficient on the
BRATS15 Challenge dataset as compared to traditional augmentation approaches.
Also, our proposed method successfully boosts a common segmentation network to
reach the state-of-the-art performance on the BRATS15 Challenge
Semantic Segmentation of Medical Images with Deep Learning: Overview
Semantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem
Breast Cancer Diagnosis in Women Using Neural Networks and Deep Learning
Breast cancer is a deadly disease affecting women around the world. It can spread rapidly into other parts of the body, causing untimely death when undetected due to rapid growth and division of cells in the breast. Early diagnosis of this disease tends to increase the survival rate of women suffering from the disease. The use of technology to detect breast cancer in women has been explored over the years. A major drawback of most research in this area is low accuracy in the detection rate of breast cancer in women. This is partly due to the availability of few data sets to train classifiers and the lack of efficient algorithms that achieve optimal results. This research aimed to develop a model that uses a machine learning approach (convolution neural network) to detect breast cancer in women with significantly high accuracy. In this paper, a model was developed using 569 mammograms of various breasts diagnosed with benign and maligned cancers. The model achieved an accuracy of 98.25% and sensitivity of 99.5% after 80 iterations.