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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 731–740 of 786 papers

TitleStatusHype
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
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