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Found 37 papers, 22 papers with code
Generative Medical Segmentation
Concretely, GMS employs a robust pre-trained Variational Autoencoder (VAE) to derive latent representations of both images and masks, followed by a mapping model that learns the transition from image to mask in the latent space.
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels.
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels.
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge
Importantly, we explore the inclusion of a transformer in the bottleneck of the U-Net architecture.
Partial supervision for the FeTA challenge 2021
Label-set loss functions allow to train deep neural networks with partially segmented images, i. e. segmentations in which some classes may be grouped into super-classes.
Homography-based Visual Servoing with Remote Center of Motion for Semi-autonomous Robotic Endoscope Manipulation
Our approach allows a surgeon to build a graph of desired views, from which, once built, views can be manually selected and automatically servoed to irrespective of robot-patient frame transformation changes.
Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction
We perform an extensive evaluation of state-of-the-art (SOTA) Deep Neural Networks (DNNs) across multiple compute regimes, finding our method transfers from our camera motion free da Vinci surgery dataset to videos of laparoscopic interventions, outperforming classical homography estimation approaches in both, precision by 41%, and runtime on a CPU by 43%.
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
The performance of deep neural networks typically increases with the number of training images.
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images.
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
We demonstrate that an STCNN trained on a HAR dataset can be used in combination with an RNN to accurately represent arbitrarily long videos of seizures.
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
We fine-tuned our model on three small annotated datasets from different institutions and on the annotated images in EPISURG, comprising 20, 33, 19 and 133 subjects.
An MRF-UNet Product of Experts for Image Segmentation
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribution data.
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data
Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e. g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths.
Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease
One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image.
MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation
In this work, we aim at improving the fetal brain segmentation for SRR in Spina Bifida.
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78. 2% across all the validated datasets.
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning
In addition to EPISURG, we used three public datasets comprising 1813 preoperative MR images for training.
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images.
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI
Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function.
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Additionally, we provide a graphical interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
Distributionally Robust Deep Learning using Hardness Weighted Sampling
In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM).
Privacy-preserving Federated Brain Tumour Segmentation
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake.
Let's agree to disagree: learning highly debatable multirater labelling
Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time.
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).
Robust parametric modeling of Alzheimer's disease progression
Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric MRI and PET biomarkers, CSF measurements, as well as cognitive tests.
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities.
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.
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
A median target registration error of 3. 6 mm on landmark centroids and a median Dice of 0. 87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.
Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
Training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory.
VTrails: Inferring Vessels with Geodesic Connectivity Trees
The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale.
Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy
Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.
NiftyNet: a deep-learning platform for medical imaging
NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Deep-learning has proved in recent years to be a powerful tool for image analysis and is now widely used to segment both 2D and 3D medical images.
What is the distribution of the number of unique original items in a bootstrap sample?
The purpose of this report is to present the distribution of the number of unique original items in a bootstrap sample clearly and concisely, with a view to enabling other machine learning researchers to understand and control this quantity in existing and future resampling techniques.
Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion
The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy.
