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Found 7 papers, 2 papers with code
Negligible effect of brain MRI data preprocessing for tumor segmentation
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability.
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
We show that the segmentation model reduces the ratio of detection disagreements from 0. 162 to 0. 085 (p < 0. 05).
Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation
We propose a loss reweighting approach to increase the ability of the network to detect small lesions.
Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting
Recent CT Metal Artifacts Reduction (MAR) methods are often based on image-to-image convolutional neural networks for adjustment of corrupted sinograms or images themselves.
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors.
Brain Tumor Image Retrieval via Multitask Learning
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting feature space as a similarity metric.
Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation
In our work, we consider segmentation of brain tumors appropriate to stereotactic radiosurgery which limits typical lesion sizes.
