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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 601–650 of 786 papers

TitleStatusHype
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Medical Image Analysis using Deep Relational Learning—0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation—0
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging—0
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology—0
Artificial Intelligence Model for Tumoral Clinical Decision Support Systems—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
Modality-Pairing Learning for Brain Tumor Segmentation—0
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images—0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Multi-Domain Image Completion for Random Missing Input Data—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation—0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
Co-Learning Feature Fusion Maps from PET-CT Images of Lung CancerCode0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET ImagingCode0
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural NetworksCode0
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)Code0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss functionCode0
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor SegmentationCode0
A New Logic For Pediatric Brain Tumor SegmentationCode0
Post-hoc Overall Survival Time Prediction from Brain MRICode0
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
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