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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 226–250 of 786 papers

TitleStatusHype
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation—0
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
A Segmentation Foundation Model for Diverse-type Tumors—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images—0
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentation—0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
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