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Found 24 papers, 12 papers with code
SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation
In our work, we leverage SAM's abstract object understanding for medical image segmentation to provide pseudo labels for semi-supervised learning, thereby mitigating the need for extensive annotated training data.
DenseSeg: Joint Learning for Semantic Segmentation and Landmark Detection Using Dense Image-to-Shape Representation
Methods: In this work, we propose a dense image-to-shape representation that enables the joint learning of landmarks and semantic segmentation by employing a fully convolutional architecture.
Danish Foundation Models
Large language models, sometimes referred to as foundation models, have transformed multiple fields of research.
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark
In an empirical study, we evaluate the performance of five state-of-the-art models for tabular data generation on eleven distinct tabular datasets.
Unsupervised 3D registration through optimization-guided cyclical self-training
State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised learning, which heavily relies on hand-crafted similarity metrics designed by domain experts, or learning from synthetic data, which introduces a domain shift.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Automated speech- and text-based classification of neuropsychiatric conditions in a multidiagnostic setting
We provide recommendations for future research in the field, suggesting increased focus on developing larger transdiagnostic datasets that include more fine-grained clinical features, and that can support the development of models that better capture the complexity of neuropsychiatric conditions and naturalistic diagnostic assessment.
TextDescriptives: A Python package for calculating a large variety of metrics from text
TextDescriptives is a Python package for calculating a large variety of metrics from text.
Chasing Clouds: Differentiable Volumetric Rasterisation of Point Clouds as a Highly Efficient and Accurate Loss for Large-Scale Deformable 3D Registration
Learning-based registration for large-scale 3D point clouds has been shown to improve robustness and accuracy compared to classical methods and can be trained without supervision for locally rigid problems.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Anatomy-guided domain adaptation for 3D in-bed human pose estimation
As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain.
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts
We build on a keypoint-based registration model, combining graph convolutions for geometric feature learning with loopy belief optimization, and propose to reduce the domain shift through self-ensembling.
Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
Extending the method to semantic features sets new stat-of-the-art performance on inter-subject abdominal CT registration.
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).
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021
Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations.
DaCy: A Unified Framework for Danish NLP
In addition, we conduct a series of tests for biases and robustness of Danish NLP pipelines through augmentation of the test set of DaNE.
Ranked #1 on
Dependency Parsing
on DaNE
Deep learning based geometric registration for medical images: How accurate can we get without visual features?
As in other areas of medical image analysis, e. g. semantic segmentation, deep learning is currently driving the development of new approaches for image registration.
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies
We believe that unsupervised domain adaptation can be beneficial in overcoming the current limitations for multimodal registration, where good metrics are hard to define.
Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings
Though, deep learning based medical image registration is currently starting to show promising advances, often, it still fells behind conventional frameworks in terms of registration accuracy.
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Mover’s discrepancies
We believe that unsupervised domain adaptation can be beneficial in overcoming the current limitations for multimodal registration, where good metrics are hard to define.
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients
Our results indicate that the inherent geometric structure of the extracted keypoints is sufficient to establish descriptive point features, which yield a significantly improved performance and robustness of our registration framework.
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
Graph convolutional networks are a new promising learning approach to deal with data on irregular domains.
