Search Results for author:
Found 33 papers, 16 papers with code
Anatomical Positional Embeddings
We train our APE model on 8400 publicly available CT images of abdomen and chest regions.
Robust Curve Detection in Volumetric Medical Imaging via Attraction Field
Understanding body part geometry is crucial for precise medical diagnostics.
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.
Redesigning Out-of-Distribution Detection on 3D Medical Images
EPD is our core contribution to the new problem design, allowing us to rank methods based on their clinical impact.
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.
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation
Segmentation-dedicated ones still achieve suboptimal performance, with the best mean FPR of 0. 31 (lower is better).
Solving Sample-Level Out-of-Distribution Detection on 3D Medical Images
Carefully discussing the limitations, we conclude that our method solves the sample-level OOD detection on 3D medical images in the current setting.
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation
When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty.
Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization
We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D simultaneously.
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.
Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency
In this paper, we show a decrease in the COVID-19 segmentation quality of the model trained on the smooth and tested on the sharp reconstruction kernels.
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
We show that the segmentation model reduces the ratio of detection disagreements from 0. 162 to 0. 085 (p < 0. 05).
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.
Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation
Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data.
Type-Centric Kotlin Compiler Fuzzing: Preserving Test Program Correctness by Preserving Types
The results show our approach to be significantly better compared to other fuzzing approaches at generating semantically valid Kotlin programs, while creating more interesting crash-inducing inputs at the same time.
Programming Languages Software Engineering
First U-Net Layers Contain More Domain Specific Information Than The Last Ones
Our results demonstrate that 1) domain-shift may deteriorate the quality even for a simple brain extraction segmentation task (surface Dice Score drops from 0. 85-0. 89 even to 0. 09); 2) fine-tuning of the first layers significantly outperforms fine-tuning of the last layers in almost all supervised domain adaptation setups.
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.
Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
Vertebral body compression fractures are reliable early signs of osteoporosis.
Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting
Recent CT Metal Artifacts Reduction (MAR) methods are often based on image-to-image convolutional neural networks for adjustment of corrupted sinograms or images themselves.
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors.
Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection
Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Brain Tumor Image Retrieval via Multitask Learning
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting feature space as a similarity metric.
Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation
In our work, we consider segmentation of brain tumors appropriate to stereotactic radiosurgery which limits typical lesion sizes.
Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging
First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit using the clinical data.
Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
In the last years, neural networks have proven to be a powerful framework for various image analysis problems.
Structural Connectome Validation Using Pairwise Classification
In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains.
Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification
In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data.
Kernel classification of connectomes based on earth mover's distance between graph spectra
In this paper, we tackle a problem of predicting phenotypes from structural connectomes.
GTApprox: surrogate modeling for industrial design
We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design.
