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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 401–410 of 786 papers

TitleStatusHype
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation—0
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation—0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning—0
Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation—0
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
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