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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 501–510 of 786 papers

TitleStatusHype
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor SegmentationCode1
Modality-aware Mutual Learning for Multi-modal Medical Image SegmentationCode1
Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma SegmentationCode0
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation—0
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic ClassificationCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
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