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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 701–750 of 786 papers

TitleStatusHype
Brain Tumor Segmentation on MRI with Missing Modalities—0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
Towards annotation-efficient segmentation via image-to-image translation—0
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical OutcomesCode0
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation—0
Deep segmentation networks predict survival of non-small cell lung cancer—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
The Liver Tumor Segmentation Benchmark (LiTS)Code1
Cascaded V-Net using ROI masks for brain tumor segmentation—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation—0
Lesion Focused Super-ResolutionCode1
Deep Recurrent Level Set for Segmenting Brain Tumors—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Glioma Segmentation with Cascaded UnetCode0
Co-Learning Feature Fusion Maps from PET-CT Images of Lung CancerCode0
Survival prediction using ensemble tumor segmentation and transfer learningCode0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural NetworkCode0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation—0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context—0
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning—0
A Hybrid Framework for Tumor Saliency Estimation—0
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor SegmentationCode0
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks—0
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRICode0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
Segmentation of Liver Lesions with Reduced Complexity Deep Models—0
Autofocus Layer for Semantic SegmentationCode0
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
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