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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 751–760 of 786 papers

TitleStatusHype
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology DatasetCode0
Selective Information Passing for MR/CT Image SegmentationCode0
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 ChallengeCode0
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?Code0
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative NormalizationCode0
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired ImagesCode0
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal EmbeddingCode0
Volumetric medical image segmentation through dual self-distillation in U-shaped networksCode0
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
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