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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 401–425 of 786 papers

TitleStatusHype
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation—0
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation—0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning—0
Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation—0
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans—0
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Benefits of Linear Conditioning with Metadata for Image Segmentation—0
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection—0
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation—0
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation—0
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
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