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Found 7 papers, 5 papers with code
Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases
A prospective cohort of 81 patients from two major US cancer centers was used to establish the reproducibility of radiomic features extracted from images reconstructed with different slice thicknesses.
Parameter-Efficient Methods for Metastases Detection from Clinical Notes
Understanding the progression of cancer is crucial for defining treatments for patients.
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.
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.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
