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Found 43 papers, 5 papers with code
FGGP: Fixed-Rate Gradient-First Gradual Pruning
In recent years, the increasing size of deep learning models and their growing demand for computational resources have drawn significant attention to the practice of pruning neural networks, while aiming to preserve their accuracy.
Joint semi-supervised and contrastive learning enables zero-shot domain-adaptation and multi-domain segmentation
We introduce SegCLR, a versatile framework designed to segment volumetric images across different domains, employing supervised and contrastive learning simultaneously to effectively learn from both labeled and unlabeled data.
Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation
This operates based on a forward model that relates the sought local values of SoS to observed speckle shifts, for which the associated image reconstruction inverse problem is solved.
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations
We introduce a contrastive learning framework for generating photorealistic images from simulated label maps, by learning from unpaired sets of both.
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains
Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data.
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
These pseudo labels are then used to train a node classification head for WSI segmentation.
Generative appearance replay for continual unsupervised domain adaptation
In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains.
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices.
Estimating Mean Speed-of-Sound from Sequence-Dependent Geometric Disparities
We demonstrate the effectiveness of our proposed method for tomographic SoS reconstruction.
Generative Feature-driven Image Replay for Continual Learning
In this work, we propose Genifer (GENeratIve FEature-driven image Replay), where a generative model is trained to replay images that must induce the same hidden features as real samples when they are passed through the classifier.
Motion Estimation for Optical Coherence Elastography Using Signal Phase and Intensity
Displacement estimation in optical coherence tomography (OCT) imaging is relevant for several potential applications, e. g. for optical coherence elastography (OCE) for corneal biomechanical characterization.
Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images
Interactive simulation of ultrasound imaging greatly facilitates sonography training.
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps
We herein propose a deep learning-based cell detection framework that can operate on large microscopy images and outputs desired probabilistic predictions by (i) integrating Bayesian techniques for the regression of uncertainty-aware density maps, where peak detection can be applied to generate cell proposals, and (ii) learning a mapping from the numerous proposals to a probabilistic space that is calibrated, i. e. accurately represents the chances of a successful prediction.
Hierarchical Graph Representations in Digital Pathology
We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions.
Utilizing Uncertainty Estimation in Deep Learning Segmentation of Fluorescence Microscopy Images with Missing Markers
However, for combinations that do not exist in a labeled training dataset, one cannot have any estimation of potential segmentation quality if that combination is encountered during inference.
Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training
Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality.
Computational Analysis of Subscapularis Tears and Pectoralis Major Transfers on Muscular Activity
Furthermore, although the PMA acts asynchronously to the subscapularis before the transfer, its patterns of activation change significantly after the transfer.
Medical Physics Computational Engineering, Finance, and Science Quantitative Methods
Influence of Rotator Cuff Integrity on Loading and Kinematics Before and After Reverse Shoulder Arthroplasty
The model was validated with respect to in-vivo glenohumeral joint reaction force (JRF) measurements, and also compared to existing clinical and biomechanical data.
Medical Physics Quantitative Methods
Quantifying Explainers of Graph Neural Networks in Computational Pathology
However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists.
Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy
Quantitative characterization of structures in acquired images often relies on automatic image analysis methods.
Reinforcement Learning of Musculoskeletal Control from Functional Simulations
With muscle activations for movements often being highly redundant, nonlinear, and time dependent, machine learning can provide a solution for their modeling and control for anatomy-specific musculoskeletal simulations.
Towards Explainable Graph Representations in Digital Pathology
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics.
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes.
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Inspired by recent advances in natural texture synthesis, we train deep neural models to generate textures by non-linearly combining learned noise frequencies.
Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction
In this work, we present for the first time a VN solution for a pulse-echo SoS image reconstruction problem using diverging waves with conventional transducers and single-sided tissue access.
Deep Image Translation for Enhancing Simulated Ultrasound Images
To incorporate anatomical information potentially lost in low quality images, we additionally provide segmentation maps to image translation.
Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation
Simulation-based ultrasound training can be an essential educational tool.
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.
Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of using bones as anatomical landmarks in surgical navigation.
Active Learning for Segmentation Based on Bayesian Sample Queries
In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning.
Deep Variational Networks with Exponential Weighting for Learning Computed Tomography
Tomographic image reconstruction is relevant for many medical imaging modalities including X-ray, ultrasound (US) computed tomography (CT) and photoacoustics, for which the access to full angular range tomographic projections might be not available in clinical practice due to physical or time constraints.
Weighted Mean Curvature
In this paper, we introduce weighted mean curvature (WMC) as a novel image prior and present an efficient computation scheme for its discretization in practical image processing applications.
SCATGAN for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks
We demonstrate robust reconstruction results, invariant to US viewing and imaging settings such as imaging direction and center frequency.
Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e. g., due to breathing.
Extending Pretrained Segmentation Networks with Additional Anatomical Structures
We propose a class-incremental segmentation framework for extending a deep network trained for some anatomical structure to yet another structure using a small incremental annotation set.
Implicit Modeling with Uncertainty Estimation for Intravoxel Incoherent Motion Imaging
Intravoxel incoherent motion (IVIM) imaging allows contrast-agent free in vivo perfusion quantification with magnetic resonance imaging (MRI).
Generative Adversarial Networks for MR-CT Deformable Image Registration
Performance for the abdominal region was similar to that of CT-MRI NMI registration (77. 4 vs. 78. 8%) when using 3D synthesizing MRIs (12 slices) and medium sized receptive fields for the discriminator.
Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging
Speed-of-sound is a biomechanical property for quantitative tissue differentiation, with great potential as a new ultrasound-based image modality.
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
Segmentation is essential for medical image analysis tasks such as intervention planning, therapy guidance, diagnosis, treatment decisions.
Learn the new, keep the old: Extending pretrained models with new anatomy and images
Deep learning has been widely accepted as a promising solution for medical image segmentation, given a sufficiently large representative dataset of images with corresponding annotations.
Herding Generalizes Diverse M -Best Solutions
We show that the algorithm to extract diverse M -solutions from a Conditional Random Field (called divMbest [1]) takes exactly the form of a Herding procedure [2], i. e. a deterministic dynamical system that produces a sequence of hypotheses that respect a set of observed moment constraints.
