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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 51–75 of 786 papers

TitleStatusHype
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
Extending nn-UNet for brain tumor segmentationCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
FIBA: Frequency-Injection based Backdoor Attack in Medical Image AnalysisCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
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