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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 231–240 of 786 papers

TitleStatusHype
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans—0
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor SegmentationCode0
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks—0
LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point PromptsCode0
Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments—0
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