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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 441–450 of 786 papers

TitleStatusHype
BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans—0
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation—0
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT—0
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT—0
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation—0
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