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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 501–525 of 786 papers

TitleStatusHype
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Transfer Learning for Brain Tumor Segmentation—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Multi-Domain Image Completion for Random Missing Input Data—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation—0
Automated ensemble method for pediatric brain tumor segmentation—0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation—0
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
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