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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 276–300 of 786 papers

TitleStatusHype
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation—0
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma—0
Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge—0
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
Brain Tumor Segmentation and Survival Prediction—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysis—0
Brain Tumor Segmentation: A Comparative Analysis—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation—0
Brain Tumor Detection Based On Symmetry Information—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification—0
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI—0
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