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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 376–400 of 786 papers

TitleStatusHype
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)—0
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
Within-Brain Classification for Brain Tumor Segmentation—0
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging—0
Mask Mining for Improved Liver Lesion Segmentation—0
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks—0
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model—0
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation—0
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT—0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation—0
Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumour Segmentation—0
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation—0
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