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Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

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Showing 676–700 of 786 papers

TitleStatusHype
Prediction of Overall Survival of Brain Tumor Patients—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Mask Mining for Improved Liver Lesion Segmentation—0
Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit—0
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation—0
An attempt at beating the 3D U-Net—0
Automatic segmentation of kidney and liver tumors in CT images—0
A Structural Graph-Based Method for MRI Analysis—0
Robustifying deep networks for image segmentation—0
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
Relevance analysis of MRI sequences for automatic liver tumor segmentation—0
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired ImagesCode0
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning—0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss—0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
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