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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 301–350 of 786 papers

TitleStatusHype
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation—0
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation—0
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation—0
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation—0
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT—0
Brain Tumor Detection Based On Symmetry Information—0
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification—0
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context—0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
Ensemble Learning and 3D Pix2Pix for Comprehensive Brain Tumor Analysis in Multimodal MRI—0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation—0
Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation—0
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation—0
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation—0
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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