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Found 7 papers, 3 papers with code
Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation
Although transformers can capture long-range features, their segmentation performance decreases with various tumor sizes due to the model sensitivity to the input patch size.
Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases
Small liver lesions common to colorectal liver metastases (CRLMs) are challenging for convolutional neural network (CNN) segmentation models, especially when we have a wide range of slice thicknesses in the computed tomography (CT) scans.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation
In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e. g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations.
Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans
Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases.
High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentation
We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images.
Generative Adversarial Network
Image-to-Image Translation
+1
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans
A comparison of these synthetic images and real images of brain tumor tissue in MR scans showed significant segmentation improvement and decreased the number of real channels for segmentation.
