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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 26–50 of 786 papers

TitleStatusHype
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT ImagesCode2
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor SegmentationCode0
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery—0
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net—0
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma—0
Towards Universal Text-driven CT Image SegmentationCode0
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor SegmentationCode0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
Clinical Inspired MRI Lesion Segmentation—0
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
Triad: Vision Foundation Model for 3D Magnetic Resonance ImagingCode1
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
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