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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 326–350 of 786 papers

TitleStatusHype
Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI—0
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy—0
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
Brain Tumor Segmentation: A Comparative Analysis—0
Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge—0
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysis—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Mask Mining for Improved Liver Lesion Segmentation—0
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
FedCostWAvg: A new averaging for better Federated Learning—0
Federated brain tumor segmentation: an extensive benchmark—0
HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
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