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Found 13 papers, 5 papers with code
M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation
Domain shift presents a significant challenge in applying Deep Learning to the segmentation of 3D medical images from sources like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT).
Medical Semantic Segmentation with Diffusion Pretrain
We introduce an auxiliary diffusion process to pretrain a model that produce generalizable feature representations, useful for a variety of downstream segmentation tasks.
The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning
Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet.
Anatomical Positional Embeddings
We train our APE model on 8400 publicly available CT images of abdomen and chest regions.
The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study
Previous studies have measured the time-saving effect of using a deep-learning-based aid (DLA) for CT interpretation.
Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation
Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes.
vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images
This paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations.
Negligible effect of brain MRI data preprocessing for tumor segmentation
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability.
Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation
We analyze a dataset of paired CT images, where smooth and sharp images were reconstructed from the same sinograms with different kernels, thus providing identical anatomy but different style.
Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation
We propose a loss reweighting approach to increase the ability of the network to detect small lesions.
CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital.
Connectivity-Driven Brain Parcellation via Consensus Clustering
We present two related methods for deriving connectivity-based brain atlases from individual connectomes.
Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms
We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context.
