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Found 286 papers, 62 papers with code
Generating synthetic electroretinogram waveforms using Artificial Intelligence to improve classification of retinal conditions in under-represented populations
Visual electrophysiology is often used clinically to determine functional changes associated with retinal or neurological conditions.
The Impact of Speech Anonymization on Pathology and Its Limits
This study investigates anonymization's impact on pathological speech across over 2, 700 speakers from multiple German institutions, focusing on privacy, pathological utility, and demographic fairness.
Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks
Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community.
Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models
Deep learning-based medical image processing algorithms require representative data during development.
The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains
Industrial process optimization and control is crucial to increase economic and ecologic efficiency.
Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation
Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e. g., layout for segmentation.
Analysing Diffusion Segmentation for Medical Images
However, there is a notable lack of analysis and discussions on the differences between diffusion segmentation and image generation, and thorough evaluations are missing that distinguish the improvements these architectures provide for segmentation in general from their benefit for diffusion segmentation specifically.
EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
However, the choice of loss function profoundly affects the reconstructed images.
AnatoMix: Anatomy-aware Data Augmentation for Multi-organ Segmentation
Multi-organ segmentation in medical images is a widely researched task and can save much manual efforts of clinicians in daily routines.
Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction
In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations.
Deep Learning Computed Tomography based on the Defrise and Clack Algorithm
The filter is designed for a specific orbit geometry and is obtained using a data-driven approach based on deep learning.
Offline Writer Identification Using Convolutional Neural Network Activation Features
Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification.
ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings
While state-of-the-art deep neural networks show impressive results for image enhancement, they often struggle to enhance real-world images.
A Spatiotemporal Illumination Model for 3D Image Fusion in Optical Coherence Tomography
This is the first optimization of a 3D inverse model in an image reconstruction context in OCT.
A 2D Sinogram-Based Approach to Defect Localization in Computed Tomography
We use a U-Net-based architecture for defect segmentation.
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework.
Integer Optimization of CT Trajectories using a Discrete Data Completeness Formulation
X-ray computed tomography (CT) plays a key role in digitizing three-dimensional structures for a wide range of medical and industrial applications.
Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization
The average organ dice of the proposed method is 0. 71 compared with 0. 63 in baseline model, indicating the enhancement of anatomical structures.
A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available.
Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification
The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues.
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cell-wise analysis.
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations
Cognitive maps, as represented by the entorhinal-hippocampal complex in the brain, organize and retrieve context from memories, suggesting that large language models (LLMs) like ChatGPT could harness similar architectures to function as a high-level processing center, akin to how the hippocampus operates within the cortex hierarchy.
Modelling of Networked Measuring Systems -- From White-Box Models to Data Based Approaches
This paradigm shift holds true in particular for the digital future of measurement in all spheres of our lives and the environment, where data provided by large and complex interconnected systems of sensors are to be analysed.
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology.
SniffyArt: The Dataset of Smelling Persons
The paper also presents a baseline analysis, evaluating the performance of representative algorithms for detection, keypoint estimation, and classification tasks, showcasing the potential of combining keypoint estimation with smell gesture classification.
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration
Incremental transfer learning, which combines peer-to-peer federated learning and domain incremental learning, can overcome the data privacy issue and meanwhile preserve model performance by using continual learning techniques.
Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings
The human brain possesses the extraordinary capability to contextualize the information it receives from our environment.
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding
The StyleGAN2 is then used to embed the photographs to its highly expressive latent space.
A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis
Unpaired image-to-image translation of retinal images can efficiently increase the training dataset for deep-learning-based multi-modal retinal registration methods.
Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing
In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood.
Handling Label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiography
If a patient exhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical norm variant of the coronary vasculature - the complexity of this procedure is increased.
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages
Parkinson's disease (PD) is a neurological disorder impacting a person's speech.
Deep Multi-Frame Filtering for Hearing Aids
Alternatively, the complex filter can be estimated via an MF minimum variance distortionless response (MVDR), or MF Wiener filter (WF).
DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal.
Ranked #10 on
Speech Enhancement
on VoiceBank + DEMAND
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution
The found radiofrequency pulse train designs generate an optimal signal for the NN to perform the SR task.
A Platform for the Biomedical Application of Large Language Models
Current-generation Large Language Models (LLMs) have stirred enormous interest in recent months, yielding great potential for accessibility and automation, while simultaneously posing significant challenges and risk of misuse.
Building a Non-native Speech Corpus Featuring Chinese-English Bilingual Children: Compilation and Rationale
This paper introduces a non-native speech corpus consisting of narratives from fifty 5- to 6-year-old Chinese-English children.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology
The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam.
Scale-Equivariant Deep Learning for 3D Data
The ability of convolutional neural networks (CNNs) to recognize objects regardless of their position in the image is due to the translation-equivariance of the convolutional operation.
Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases.
Task-based Generation of Optimized Projection Sets using Differentiable Ranking
We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis.
BOSS: Bones, Organs and Skin Shape Model
Objective: A digital twin of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions.
Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis.
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image.
Retinal blood flow speed quantification at the capillary level using temporal autocorrelation fitting OCTA
Optical coherence tomography angiography (OCTA) can visualize vasculature structures, but provides limited information about the blood flow speeds.
Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning
We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment.
Word class representations spontaneously emerge in a deep neural network trained on next word prediction
Strikingly, we find that the internal representations of nine-word input sequences cluster according to the word class of the tenth word to be predicted as output, even though the neural network did not receive any explicit information about syntactic rules or word classes during training.
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Nevertheless, the prediction of high-resolution transient CFD simulations for complex vascular geometries poses a challenge to conventional deep learning models.
Optimizing CT Scan Geometries With and Without Gradients
In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image.
Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography
By reconstructing independent stacks of projection data, a self-supervised loss is calculated in the CT image domain and used to directly optimize projection image intensities to match the missing tomographic views constrained by the projection geometry.
AMD-HookNet for Glacier Front Segmentation
are present in the data), which exacerbates the difficulty of accurate segmentation.
Ranked #1 on
Calving Front Delineation In Synthetic Aperture Radar Imagery With Fixed Training Amount
on CaFFe
Calving Front Delineation In Synthetic Aperture Radar Imagery With Fixed Training Amount
Segmentation
ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition
The Odeuropa Challenge on Olfactory Object Recognition aims to foster the development of object detection in the visual arts and to promote an olfactory perspective on digital heritage.
Transfer Learning for Olfactory Object Detection
We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining.
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning
Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy planning.
Ranked #1 on
Breast Cancer Detection
on CMMD
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset
Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in- and cross-domain.
Democratising Knowledge Representation with BioCypher
Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods.
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising
We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss.
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone.
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology
Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably.
Metal-conscious Embedding for CBCT Projection Inpainting
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images.
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation
Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences.
Ranked #2 on
3D Human Pose Estimation
on 3DPW
(using extra training data)
An unobtrusive quality supervision approach for medical image annotation
Users could not detect 52. 12% of generated images by DM proofing the feasibility to replace the original cells with synthetic cells without being noticed.
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks
We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology.
Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples
For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders.
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems
The availability of large-scale chest X-ray datasets is a requirement for developing well-performing deep learning-based algorithms in thoracic abnormality detection and classification.
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data.
Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts
The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of 'animal space' based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i. e. nearest neighbor in feature space.
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble
A minimal mean error of 7. 54% for MAPE and 417kW for RMSE is achieved with the proposed framework in comparison to all other methods.
ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints
Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists.
Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion
Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training.
Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy
Therefore, there is an urgent need to obfuscate the biometric information appearing in the images.
Metal Inpainting in CBCT Projections Using Score-based Generative Model
During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems.
A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT
Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology.
Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy
For a subgroup of 10 cases, intraobserver variability was compared to the average DL autosegmentation accuracy on the original and recontoured set of expert segmentations.
A Multi-modal Registration and Visualization Software Tool for Artworks using CraquelureNet
For art investigations of paintings, multiple imaging technologies, such as visual light photography, infrared reflectography, ultraviolet fluorescence photography, and x-radiography are often used.
SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Here we introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data for DL systems.
Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network
Our method extracts convolutional features from the vessel structure for keypoint detection and description and uses a graph neural network for feature matching.
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.
AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound
On the measured data, the predictions of the proposed method are close to the expected values with MAPE of 1. 1%.
PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis
This paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus.
ICC++: Explainable Image Retrieval for Art Historical Corpora using Image Composition Canvas
In this work, we present a novel approach called Image Composition Canvas (ICC++) to compare and retrieve images having similar compositional elements.
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation
Then, to align the source and target features and tackle the memory issue of the traditional contrastive loss, we propose the centroid-based contrastive learning (CCL) and a centroid norm regularizer (CNR) to optimize the contrastive pairs in both direction and magnitude.
DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images
Reliably detecting diseases using relevant biological information is crucial for real-world applicability of deep learning techniques in medical imaging.
Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification
In this work, we use for HSRS image classification a special class of deep neural networks, namely a Bayesian neural network (BNN).
DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio
This makes the algorithm applicable to run on embedded devices in real-time.
Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection
The best configuration detects LVOs with an AUC of 0. 91, LVOs in the ICA with an AUC of 0. 96, and in the MCA with 0. 91 while accurately predicting the affected side.
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning
We validate our approach by using the CBIS-DDSM dataset for two classification tasks.
An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes
In the work at hand, we place the algorithm in a clinical context by evaluating the labeling and occlusion detection on stroke patients, where we have achieved labeling sensitivities comparable to other works between 92\,\% and 95\,\%.
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification
With iterative continual learning (i. e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0. 914, which is identical to the sensitivity using mixed data for training.
The effect of speech pathology on automatic speaker verification -- a large-scale study
Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data.
Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment
Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.
Predictive coding and stochastic resonance as fundamental principles of auditory perception
How is information processed in the brain during perception?
Mitosis domain generalization in histopathology images -- The MIDOG challenge
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices
Collecting speech data is an important step in training speech recognition systems and other speech-based machine learning models.
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition
Compared to using Fbank features, XLSR-based features reduced WERs by 6. 8%, 22. 0%, and 7. 0% for the UASpeech, PC-GITA, and EasyCall corpus, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning
The tuning is carried out by two deep CNNs.
Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis
AUROC was 75% for seropositive RA vs. PsA, 74% for seronegative RA vs. PsA and 67% for seropositive vs. seronegative RA.
Simulation-Driven Training of Vision Transformers Enabling Metal Segmentation in X-Ray Images
Since the generation of high quality clinical training is a constant challenge, this study proposes to generate simulated X-ray images based on CT data sets combined with self-designed computer aided design (CAD) implants and make use of convolutional neural network (CNN) and vision transformer (ViT) for metal segmentation.
Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns—How Neural Networks Can Tell Us Where to “Deep Dive” Clinically
Methods: We trained a novel neural network on 3D articular bone shapes of hand joints of RA and PsA patients as well as HC.
Neural Network based Successor Representations of Space and Language
We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.
Deep learning-guided weighted averaging for signal dropout compensation in diffusion-weighted imaging of the liver
Methods: Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed which aims to suppress the contribution of image regions affected by signal dropouts.
Learning Perspective Deformation in X-Ray Transmission Imaging
The complementary view setting provides a practical way to identify perspectively deformed structures by assessing the deviation between the two views.
CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling
With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high.
SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks
With the increasing number of online learning material in the web, search for specific content in lecture videos can be time consuming.
Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness
Ultrasound b-mode imaging is a qualitative approach and diagnostic quality strongly depends on operators' training and experience.
Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation
This restricts the development of UDA methods for new domains.
Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging.
Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society.
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.
Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging
We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy.
Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations
Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data.
Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation
When evaluating the approaches on fully manually annotated images, we observe that the autoencoder-based superpixels achieve a 23% increase in boundary F1 score compared to the baseline SLIC superpixels.
Segmentation of the Carotid Lumen and Vessel Wall using Deep Learning and Location Priors
In this report we want to present our method and results for the Carotid Artery Vessel Wall Segmentation Challenge.
A Keypoint Detection and Description Network Based on the Vessel Structure for Multi-Modal Retinal Image Registration
Our method demonstrates the best registration performance on our and a public multi-modal dataset in comparison to competing methods.
Deep learning for brain metastasis detection and segmentation in longitudinal MRI data
To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels.
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
Instead, we frame fault detection as more realistic unsupervised domain adaptation problem where we train on labelled data of one source PV plant and make predictions on another target plant.
Does Proprietary Software Still Offer Protection of Intellectual Property in the Age of Machine Learning? -- A Case Study using Dual Energy CT Data
In the domain of medical image processing, medical device manufacturers protect their intellectual property in many cases by shipping only compiled software, i. e. binary code which can be executed but is difficult to be understood by a potential attacker.
View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT -- An Investigation of Potential Improvement
Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR).
Detection of Large Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations
Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0. 85 from a baseline value of 0. 56 using no augmentation.
Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model
In combating climate change, an effective demand-based energy supply operation of the district energy system (DES) for heating or cooling is indispensable.
First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain
Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort.
DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering
In this work, we propose DeepFilterNet, a two stage speech enhancement framework utilizing deep filtering.
Automatic Plane Adjustment of Orthopedic Intra-operative Flat Panel Detector CT-Volumes
The most important benefit of the MTL approach is that it is a single network for standard plane regression for all body regions with a reduced number of stored parameters.
Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery
In this work, automated RP detection has been introduced by the proposed framework and demonstrated feasibility, robustness, and applicability for static imaging acquisitions.
Module-Power Prediction from PL Measurements using Deep Learning
While these areas can be easily identified from electroluminescense (EL) images, this is much harder for photoluminescence (PL) images.
InSE-NET: A Perceptually Coded Audio Quality Model based on CNN
Automatic coded audio quality assessment is an important task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen codecs, bitrates, content-types, and a lack of flexibility of existing approaches.
Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery
For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed.
Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens
Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes.
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future
In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging.
Deep Iterative 2D/3D Registration
A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy.
Robust quad-tree based registration of whole slide images
The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology.
Automatic and explainable grading of meningiomas from histopathology images
In a first approach, image patches are sampled from this region and regression is based on morphological features encoded by a ResNet-based network.
FlexParser -- the adaptive log file parser for continuous results in a changing world
Any modern system writes events into files, called log files.
SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators
As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation.
Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural network
The algorithm is trained on DW liver data of 60 volunteers and evaluated on retrospectively and prospectively sub-sampled data of different anatomies and resolutions.
Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained.
Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities.
MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks
Our approach is motivated by style transfer networks, whereas the "style" for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on.
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.
Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data
Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0. 9940 and a classification accuracy of 95. 55%.
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces.
Pixel-wise Distance Regression for Glacier Calving Front Detection and Segmentation
In this work, we propose to mitigate the class-imbalance between the calving front class and the non-calving front class by reformulating the segmentation problem into a pixel-wise regression task.
Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
We perform a simulation study using real motion recorded with an optical tracking system.
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks
A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis.
On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery
Moreover, we propose an improvement to the distance map-based binary cross-entropy (BCE) loss function.
Learning the Update Operator for 2D/3D Image Registration
We aim to address this gap by incorporating traditional methods in deep neural networks using known operator learning.
Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering
First, a statistical human shape model of the human anatomy and second, a differentiable X-ray renderer.
SkinScan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation
The utilization of computational photography becomes increasingly essential in the medical field.
Learning-Based Patch-Wise Metal Segmentation with Consistency Check
Metal implants that are inserted into the patient's body during trauma interventions cause heavy artifacts in 3D X-ray acquisitions.
X-ray Scatter Estimation Using Deep Splines
Algorithmic X-ray scatter compensation is a desirable technique in flat-panel X-ray imaging and cone-beam computed tomography.
Medical Physics Image and Video Processing
Learning to be EXACT, Cell Detection for Asthma on Partially Annotated Whole Slide Images
We evaluated our pipeline in a cross-validation setup with a fixed training set using a dataset of six equine WSIs of which four are partially annotated and used for training, and two fully annotated WSI are used for validation and testing.
Coronary Plaque Analysis for CT Angiography Clinical Research
However, to enable clinical research with the help of these algorithms, a software solution, which enables manual correction, comprehensive visual feedback and tissue analysis capabilities, is needed.
Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm
Supervised machine learning requires a large amount of labeled data to achieve proper test results.
Glacier Calving Front Segmentation Using Attention U-Net
An essential climate variable to determine the tidewater glacier status is the location of the calving front position and the separation of seasonal variability from long-term trends.
Bayesian U-Net for Segmenting Glaciers in SAR Imagery
It is therefore important to precisely monitor the position of the calving front.
Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors
For this study, we created the first open source data-set with 19, 983 annotations of BiNC and 1, 416 annotations of MuNC in 32 histological whole slide images of ccMCT.
2-D Respiration Navigation Framework for 3-D Continuous Cardiac Magnetic Resonance Imaging
Continuous protocols for cardiac magnetic resonance imaging enable sampling of the cardiac anatomy simultaneously resolved into cardiac phases.
Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence.
Combined subleading high-energy logarithms and NLO accuracy for W production in association with multiple jets
Large logarithmic corrections in $\hat s/p_t^2$ lead to substantial variations in the perturbative predictions for inclusive $W$-plus-dijet processes at the Large Hadron Collider.
High Energy Physics - Phenomenology
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning
(2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6-5th century BCE with person and pose annotations.
Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization
For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction.
How Many Annotators Do We Need? -- A Study on the Influence of Inter-Observer Variability on the Reliability of Automatic Mitotic Figure Assessment
Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours.
Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health.
Joint Super-Resolution and Rectification for Solar Cell Inspection
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants.
Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising
Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature.
Reconstruction of Voxels with Position- and Angle-Dependent Weightings
In this work we first formulate this reconstruction problem in terms of a system matrix and weighting part.
ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments
In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification.
Automatic CAD-RADS Scoring Using Deep Learning
Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD).
Deep Learning-based Pipeline for Module Power Prediction from EL Measurements
However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string.
Graph convolutional regression of cardiac depolarization from sparse endocardial maps
The training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts.
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems
The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93. 0% and 80. 7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections respectively.
Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors
These compositions are useful in analyzing the interactions in an image to study artists and their artworks.
A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
We achieved a mean F1-score of 0. 791 on the test set and of up to 0. 696 on a human breast cancer dataset.
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i. e. verification of screw placement.
The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks
Due to the multiple imperfections during the signal acquisition, Electrocardiogram (ECG) datasets are typically contaminated with numerous types of noise, like salt and pepper and baseline drift.
Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation
However, because of the limited availability of scans containing nodules and the subtle properties of nodules in CXRs, state-of-the-art methods do not perform well on nodule classification.
The Notary in the Haystack -- Countering Class Imbalance in Document Processing with CNNs
In classification, notarial instruments are distinguished from other documents, while the notary sign is separated from the certificate in the segmentation task.
Re-ranking for Writer Identification and Writer Retrieval
We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available.
Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels
To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms.
Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
In our proposed multi-stage algorithm, these signals are transformed to the global coordinate system of the CT scan and applied for motion compensation during reconstruction.
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization
Denoising of clinical CT images is an active area for deep learning research.
JBFnet -- Low Dose CT Denoising by Trainable Joint Bilateral Filtering
JBFnet is split into four filtering blocks, each of which performs Joint Bilateral Filtering.
Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning
Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose.
CLC: Complex Linear Coding for the DNS 2020 Challenge
Based on the published test set, we outperform the baseline w. r. t.
Appearance Learning for Image-based Motion Estimation in Tomography
To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach.
Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images
For example, for truncated data, DCR achieves a mean root-mean-square error of 24 HU and a mean structure similarity index of 0. 999 inside the field-of-view for different patients in the noisy case, while the state-of-the-art U-Net method achieves 55 HU and 0. 995 respectively for these two metrics.
Projection Inpainting Using Partial Convolution for Metal Artifact Reduction
In this work, partial convolution is applied for projection inpainting, which only relies on valid pixels values.
EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control
In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides
However, a significant percentage of the deliberately introduced false labels was not identified by the experts.
Human-Computer Interaction Image and Video Processing
Recognizing Characters in Art History Using Deep Learning
We present experiments and analysis on three different models and show that the model trained on domain related data gives the best performance for recognizing character.
Will we ever have Conscious Machines?
The question of whether artificial beings or machines could become self-aware or consciousness has been a philosophical question for centuries.
Spatio-Temporal Handwriting Imitation
Then, a method for online handwriting synthesis is used to produce a new realistic-looking text primed with the online input sequence.
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
In this study, we investigate the impact of various data augmentation algorithms, e. g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance problem.
Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm
To this end, we apply normalized Lp normalization to aggregate the activation maps into single scores for classification.
CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding
To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding.
An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process
For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity.
Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning
Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images.
Prediction of MRI Hardware Failures based on Image Features using Time Series Classification
Thus, we used image features and their variation over time to predict coil damage.
