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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 631–640 of 786 papers

TitleStatusHype
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation—0
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images—0
Technical report: Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated FusionCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
Stan: Small tumor-aware network for breast ultrasound image segmentationCode1
Distributionally Robust Deep Learning using Hardness Weighted SamplingCode0
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIsCode1
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