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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 301–310 of 786 papers

TitleStatusHype
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
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)—0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation—0
BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark—0
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