Tumor Segmentation
222 papers with code • 3 benchmarks • 9 datasets
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.
Benchmarks
These leaderboards are used to track progress in Tumor Segmentation
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Libraries
Use these libraries to find Tumor Segmentation models and implementations
BraTS 2015
LiTS17
DigestPath
BraTS 2016
BraTS 2014
CSAW-S
NSCLC-Radiogenomics
CoCaHis
Most implemented papers
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
shu-hai/two-stage-VAE-Attention-gate-BraTS2020
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Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning.
TBraTS: Trusted Brain Tumor Segmentation
cocofeat/tbrats
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In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution.
MRI Tumor Segmentation with Densely Connected 3D CNN
lelechen63/MRI-tumor-segmentation-Brats
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The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients.
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Stan: Small tumor-aware network for breast ultrasound image segmentation
sudohainguyen/STAN-small-tumor-aware-network
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The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
mdciri/Vox2Vox
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Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i. e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core.
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation
qubvel/segmentation_models.pytorch
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IEEE Access 2020
In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation.
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)
yousefis/DenseUnet_Esophagus_Segmentation
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The proposed network achieved a $\mathrm{DSC}$ value of $0. 79 \pm 0. 20$, a mean surface distance of $5. 4 \pm 20. 2mm$ and $95\%$ Hausdorff distance of $14. 7 \pm 25. 0mm$ for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone.
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e. g., X-Ray, CT, and MRI) and analysis tasks (e. g., classification, detection, and segmentation).
Diffusion Models for Implicit Image Segmentation Ensembles
juliawolleb/diffusion-based-segmentation
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By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images.
