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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 451–500 of 786 papers

TitleStatusHype
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI—0
Automatic segmentation of kidney and liver tumors in CT images—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method—0
Weakly supervised pan-cancer segmentation tool—0
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks—0
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
MBA-Net: SAM-driven Bidirectional Aggregation Network for Ovarian Tumor Segmentation—0
Deep segmentation networks predict survival of non-small cell lung cancer—0
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
Medical Image Analysis using Deep Relational Learning—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
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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