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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 191–200 of 786 papers

TitleStatusHype
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point PromptsCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
A fuzzy rank-based ensemble of CNN models for MRI segmentationCode0
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural NetworkCode0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 ChallengeCode0
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
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