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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 61–70 of 786 papers

TitleStatusHype
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
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