… using convolutional neural networks for brain tumor segmentation … CNN architectures for
tackling brain tumor segmentation. Our architectures exploit the most recent advances in CNN …

… With accurate segmentation, clinical diagnostic not only … In this paper, a CNN architecture
for brain tumor segmentation … important when it comes to tumor segmentation task. The use of …

… deep … tumor segmentation tasks; 2) we introduce multi-scale receptive fields for accurate
voxel classification and efficient dense inference; 3) we propose a hierarchical segmentation …

… We use a Deep Convolutional Neural Network (CNN) for each modality to learn good …
Each CNN is trained separately to classify a pixel as one of non-tumor, necrosis, edema, …

… Recently, deep learning, especially convolutional neural networks, has achieved the … the
tumor segmentation methods based on deep learning from technique view and tumor view, …

… To further classify and predict brain Tumors, use brain Tumor segmentation to extract Tumor
… -learning and deep learning approaches have been developed for segmenting Tumor cells. …

… The performance of the deep CNN can be improved (or optimized) by training the network
on a large dataset. Training involves finding the parameters θ of the model that significantly …

… explored in Deep Learning methods for brain tumor segmentation. Also, we investigated
the potential of deep architectures through small kernels by comparing our deep CNN with …

… Most of the above CNN brain tumor segmentation methods assumed that each voxel's
label is independent, and they didn't take the appearance and spatial consistency into …

… liver tumor segmentation using deep learning … tumor segmentation using CNN. This
model consists of a three-dimensional dual-path multiscale convolutional neural network (TDP-CNN)…