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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 251–275 of 786 papers

TitleStatusHype
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node MetastasisCode0
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple HospitalsCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
Segment Anything Model for Brain Tumor Segmentation—0
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT ImagesCode0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
Towards Optimal Patch Size in Vision Transformers for Tumor SegmentationCode0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps SegmentationCode0
Automated ensemble method for pediatric brain tumor segmentation—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation—0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
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