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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 591–600 of 786 papers

TitleStatusHype
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT—0
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation—0
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
MBA-Net: SAM-driven Bidirectional Aggregation Network for Ovarian Tumor Segmentation—0
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation—0
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