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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 651–660 of 786 papers

TitleStatusHype
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotationsCode0
SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image AnalysisCode0
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural NetworkCode0
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic VolumesCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
Hybrid-Fusion Transformer for Multisequence MRICode0
Progressive Dual Priori Network for Generalized Breast Tumor SegmentationCode0
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIsCode0
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT ImagesCode0
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