Search Results for author:
Found 289 papers, 62 papers with code
Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images
Automated segmentation of cells is therefore a key step in automating the visual inspection workflow.
Action Learning for 3D Point Cloud Based Organ Segmentation
From this we conclude that our method is robust, and we believe that our method can be successfully applied to many more applications, in particular, in the interventional imaging space.
Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers.
Motion Artifact Detection in Confocal Laser Endomicroscopy Images
Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing non-invasive examination of the mucosa on a (sub)cellular level, has proven to be a valuable diagnostic tool in gastroenterology and shows promising results in various anatomical regions including the oral cavity.
Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model
Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene.
Projection image-to-image translation in hybrid X-ray/MR imaging
Our approach is capable of creating X-ray projection images with natural appearance.
MR to X-Ray Projection Image Synthesis
The perceptual-loss showed to be able to preserve most of the high-frequency details in the projection images and, thus, is recommended for the underlying task and similar problems.
Closing the Calibration Loop: An Inside-out-tracking Paradigm for Augmented Reality in Orthopedic Surgery
In percutaneous orthopedic interventions the surgeon attempts to reduce and fixate fractures in bony structures.
Classification of breast cancer histology images using transfer learning
Breast cancer is one of the leading causes of mortality in women.
Classification
Classification Of Breast Cancer Histology Images
+3
Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors.
Scale-Space Anisotropic Total Variation for Limited Angle Tomography
Hence, the main purpose of this paper is to reduce streak artifacts at various scales.
Encoding CNN Activations for Writer Recognition
The encoding of local features is an essential part for writer identification and writer retrieval.
Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions
Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.
Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
The proposed method achieved an average Dice coefficient of 0. 7808$\pm$0. 1729 and Jaccard index of 0. 6704$\pm$0. 2167.
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
This synthetic data is sampled from a GMM fitted to each class of the limited training data.
Classification Of Hyperspectral Images
General Classification
+1
Image Registration for the Alignment of Digitized Historical Documents
In this work, we conducted a survey on different registration algorithms and investigated their suitability for hyperspectral historical image registration applications.
Sketch Layer Separation in Multi-Spectral Historical Document Images
High-resolution imaging has delivered new prospects for detecting the material composition and structure of cultural treasures.
Precision Learning: Towards Use of Known Operators in Neural Networks
We demonstrate that use of known transforms is able to change maximal error bounds.
Robust Seed Mask Generation for Interactive Image Segmentation
In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images.
Frangi-Net: A Neural Network Approach to Vessel Segmentation
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter.
Precision Learning: Reconstruction Filter Kernel Discretization
In this paper, we present substantial evidence that a deep neural network will intrinsically learn the appropriate way to discretize the ideal continuous reconstruction filter.
Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
In this paper, we proposed a 3D FCN based method for automatic multi-organ segmentation in DECT.
UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model
Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as an active user model for training.
Unsupervised Feature Learning for Writer Identification and Writer Retrieval
The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI).
A Guided Spatial Transformer Network for Histology Cell Differentiation
The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image.
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
We find that the network trained on the oral cavity data reaches an accuracy of 89. 45% and an area-under-the-curve (AUC) value of 0. 955, when applied on the vocal cords data.
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion.
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction
Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements.
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Motion estimation across low-resolution frames and the reconstruction of high-resolution images are two coupled subproblems of multi-frame super-resolution.
Super-Resolved Retinal Image Mosaicing
The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons.
3-D/2-D Registration of Cardiac Structures by 3-D Contrast Agent Distribution Estimation
Using a combination of both methods, our evaluation on 11 well-contrasted clinical datasets yielded an error of 7. 9+/-6. 3 mm over all frames.
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings
Embedding data into vector spaces is a very popular strategy of pattern recognition methods.
Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
For single-vertebra registration, we achieve an accuracy of 0. 74$\pm$0. 26 mm and highly improved robustness.
SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
Early detection and segmentation of skin lesions are crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients.
Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning
For C-arm repositioning to a particular target view, the recorded C-arm pose is restored as a virtual object and visualized in an AR environment, serving as a perceptual reference for the technician.
Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion
The results demonstrate that the proposed method is superior to ray-by-ray interpolation and is able to deliver sharper images using the same amount of parallel-beam input projections which is crucial for interventional applications.
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images
Both approaches are trained on 1, 968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules.
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction
This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.
A Multi-task Framework for Skin Lesion Detection and Segmentation
This underlines the need for an accurate and automatic approach to skin lesion segmentation.
Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge.
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data
One interesting result is that top-performing methods on simulated data may be surpassed by others on real data.
Augmented Mitotic Cell Count using Field Of Interest Proposal
This map is in a second step used to construct a mitotic activity estimate.
A Gentle Introduction to Deep Learning in Medical Image Processing
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications.
Field Of Interest Proposal for Augmented Mitotic Cell Count: Comparison of two Convolutional Networks
For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity.
A 3-D Projection Model for X-ray Dark-field Imaging
A key step of the reconstruction algorithm is the inversion of a forward projection model.
The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data
In this work, a balanced random forest approach for WEKA is proposed.
Erlangen-CLP: A Large Annotated Corpus of Speech from Children with Cleft Lip and Palate
In this paper we describe Erlangen-CLP, a large speech database of children with Cleft Lip and Palate.
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage.
Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities.
A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer.
Analysis by Adversarial Synthesis -- A Novel Approach for Speech Vocoding
Recently, autoregressive deep generative models such as WaveNet and SampleRNN have been used as speech vocoders to scale up the perceptual quality of the reconstructed signals without increasing the coding rate.
RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting
Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary.
A Divide-and-Conquer Approach towards Understanding Deep Networks
We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators.
Multi-task Localization and Segmentation for X-ray Guided Planning in Knee Surgery
A deep multi-task stacked hourglass network is trained on 149 conventional lateral X-ray images to jointly localize two femoral landmarks, to predict a region of interest for the posterior femoral cortex tangent line, and to perform semantic segmentation of the femur, patella, tibia, and fibula with adaptive task complexity weighting.
Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features.
Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues.
Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction.
Image Quality Assessment for Rigid Motion Compensation
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures.
Superpixel-Based Background Recovery from Multiple Images
Background candidate images are obtained from input raw images with the masks.
Field of View Extension in Computed Tomography Using Deep Learning Prior
In computed tomography (CT), data truncation is a common problem.
What Do We Really Need? Degenerating U-Net on Retinal Vessel Segmentation
Results show that for retinal vessel segmentation on DRIVE database, U-Net does not degenerate until surprisingly acute conditions: one level, one filter in convolutional layers, and one training sample.
Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing
As such, it is end-to-end trainable, circumvents the use of hand-crafted and potentially complex algorithms, and mitigates error propagation.
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
In medical imaging, this lack of comprehensibility of the results is a sensitive issue.
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging.
Learning New Tricks from Old Dogs -- Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment
We were able to show that domain adversarial training considerably improves accuracy when applying mitotic figure classification learned from the canine on the human data sets (up to +12. 8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.
Deep autofocus with cone-beam CT consistency constraint
To adapt the backprojection operation accordingly, a motion estimation strategy is necessary.
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis.
Epoch-wise label attacks for robustness against label noise
With a simple one-class problem, the classification of tuberculosis, we measure the performance on a clean evaluation set when training with label-corrupt data.
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation
In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality.
Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics
A second approach is based on deep learning and relies on centerline extraction as sole prerequisite.
Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans
Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research.
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach
In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution.
Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning
The other data level uses matrices which represent the overall coil condition and feeds a different neural network.
COPD Classification in CT Images Using a 3D Convolutional Neural Network
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
Joint Super-Resolution and Rectification for Solar Cell Inspection
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants.
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.
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.
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.
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
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.
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.
Bayesian U-Net for Segmenting Glaciers in SAR Imagery
It is therefore important to precisely monitor the position of the calving front.
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.
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.
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-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.
SkinScan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation
The utilization of computational photography becomes increasingly essential in the medical field.
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.
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.
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.
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.
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.
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.
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.
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.
FlexParser -- the adaptive log file parser for continuous results in a changing world
Any modern system writes events into files, called log files.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society.
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.
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.
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.
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.
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.
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.
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.
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.
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning
The tuning is carried out by two deep CNNs.
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
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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%.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
Transfer Learning for Olfactory Object Detection
We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
