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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 151–200 of 786 papers

TitleStatusHype
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated FusionCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
Stan: Small tumor-aware network for breast ultrasound image segmentationCode1
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIsCode1
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and AnalysisCode1
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 ChallengeCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
The Liver Tumor Segmentation Benchmark (LiTS)Code1
Lesion Focused Super-ResolutionCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation—0
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling—0
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal EmbeddingCode0
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation—0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
TAGS: 3D Tumor-Adaptive Guidance for SAM—0
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation—0
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature FusionCode0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis—0
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing—0
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI—0
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
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
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
Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma—0
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—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
Clinical Inspired MRI Lesion Segmentation—0
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
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