SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–750 of 786 papers

TitleStatusHype
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentation—0
CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
Decentralized Differentially Private Segmentation with PATE—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
Synthetic Data as Validation—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation—0
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation—0
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans—0
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation—0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence—0
Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment—0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
Show:102550
← PrevPage 15 of 16Next →

No leaderboard results yet.