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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 501–525 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
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Medical Image Segmentation Using Squeeze-and-Expansion TransformersCode1
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
The Federated Tumor Segmentation (FeTS) ChallengeCode1
Weakly supervised pan-cancer segmentation tool—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
mlf-core: a framework for deterministic machine learningCode1
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Transfer learning for automatic brain tumor classification Using MRI Images.—0
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