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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 451–475 of 786 papers

TitleStatusHype
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT ImagesCode1
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation TaskCode1
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challengeCode1
Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled DataCode1
Teacher-Student Architecture for Mixed Supervised Lung Tumor SegmentationCode1
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
Extending nn-UNet for brain tumor segmentationCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
FIBA: Frequency-Injection based Backdoor Attack in Medical Image AnalysisCode1
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Exploring Feature Representation Learning for Semi-supervised Medical Image SegmentationCode0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
FedCostWAvg: A new averaging for better Federated Learning—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
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