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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 291–300 of 786 papers

TitleStatusHype
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging—0
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
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
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