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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 491–500 of 786 papers

TitleStatusHype
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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