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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 551–575 of 786 papers

TitleStatusHype
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging—0
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
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
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma SegmentationCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation—0
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
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
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
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
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