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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 651–700 of 786 papers

TitleStatusHype
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database—0
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images—0
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep LearningCode0
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound ImagesCode0
Organ At Risk Segmentation with Multiple Modality—0
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor SegmentationCode0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Segmentation and Survival Prediction—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks—0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation—0
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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