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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 701–725 of 786 papers

TitleStatusHype
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—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
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—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
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—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
CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—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
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
Decentralized Differentially Private Segmentation with PATE—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
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