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Found 16 papers, 7 papers with code
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.
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.
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.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
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.
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.
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.
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.
Sparse Group Inductive Matrix Completion
We consider the problem of matrix completion with side information (\textit{inductive matrix completion}).
