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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 476–500 of 786 papers

TitleStatusHype
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Optimized U-Net for Brain Tumor SegmentationCode0
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRICode0
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images—0
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT—0
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Self-supervised Tumor Segmentation through Layer Decomposition—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation—0
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging—0
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
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