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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 311–320 of 786 papers

TitleStatusHype
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation—0
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT ImagesCode0
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 ChallengeCode0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
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