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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 476–500 of 786 papers

TitleStatusHype
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks—0
MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging—0
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data—0
Artificial Intelligence Model for Tumoral Clinical Decision Support Systems—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Modality-Pairing Learning for Brain Tumor Segmentation—0
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images—0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
Automated head and neck tumor segmentation from 3D PET/CT—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Brain MRI Tumor Segmentation with Adversarial Networks—0
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