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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 26–50 of 786 papers

TitleStatusHype
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
3D Self-Supervised Methods for Medical ImagingCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
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