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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 691–700 of 786 papers

TitleStatusHype
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
Relevance analysis of MRI sequences for automatic liver tumor segmentation—0
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired ImagesCode0
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning—0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss—0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
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