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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 301–310 of 786 papers

TitleStatusHype
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features—0
Federated brain tumor segmentation: an extensive benchmark—0
FedPID: An Aggregation Method for Federated Learning—0
Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation—0
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