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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 201–225 of 786 papers

TitleStatusHype
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging—0
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans—0
A Structural Graph-Based Method for MRI Analysis—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
Clinical Inspired MRI Lesion Segmentation—0
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans—0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—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
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images—0
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
A Segmentation Foundation Model for Diverse-type Tumors—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
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