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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 676–700 of 786 papers

TitleStatusHype
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths—0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery—0
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context—0
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation—0
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images—0
Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments—0
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology—0
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images—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
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images—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
Clinical Inspired MRI Lesion Segmentation—0
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation—0
Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities—0
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
Context Aware 3D UNet for Brain Tumor Segmentation—0
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