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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 151–175 of 786 papers

TitleStatusHype
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI—0
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed TomographyCode1
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor SynthesisCode2
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI—0
SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform RegressionCode1
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation—0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences—0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation—0
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI—0
MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation—0
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural NetworksCode0
PAM-UNet: Shifting Attention on Region of Interest in Medical Images—0
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