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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 401–450 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
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers—0
Joint Liver Lesion Segmentation and Classification via Transfer Learning—0
Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation—0
Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge—0
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Large-Kernel Attention for 3D Medical Image Segmentation—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation—0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans—0
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation—0
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT—0
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT—0
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation—0
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