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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 331–340 of 786 papers

TitleStatusHype
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Self-calibrated convolution towards glioma segmentation—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model—0
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detectionCode0
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