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Found 331 papers, 76 papers with code
Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Data
The diverse nature of MRI acquisitions with varying contrasts and orientation introduce complexity in identifying hemorrhage using neural networks.
Perceptual Implications of Automatic Anonymization in Pathological Speech
This study presents the first comprehensive human-centered analysis of anonymized pathological speech, using a structured perceptual protocol involving ten native and non-native German listeners with diverse linguistic, clinical, and technical backgrounds.
Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral Filtering
Effective denoising is crucial in low-dose CT to enhance subtle structures and low-contrast lesions while preventing diagnostic errors.
Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep Learning
The model strategically manipulates geometry and texture features extracted from an initial projection to match new viewing angles.
A Category-Fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images
In this study, we propose a novel deep learning-based category and fragment segmentation (CFS) framework for the automatic segmentation of pelvic bone fragments in 2D X-ray images.
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities
Pretrain techniques, whether supervised or self-supervised, are widely used in deep learning to enhance model performance.
From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine
The field has advanced rapidly, evolving from text-only large language models for tasks such as clinical documentation and decision support to multimodal AI systems capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model.
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma
Importantly, the proposed approach relies on readily available anatomical MRI sequences, clinical data, and RT treatment planning information, enhancing its clinical feasibility.
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features
To restore proper blood flow in blocked coronary arteries via angioplasty procedure, accurate placement of devices such as catheters, balloons, and stents under live fluoroscopy or diagnostic angiography is crucial.
Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model
The differentiable shift-variant filtered backprojection (FBP) model enables the reconstruction of cone-beam computed tomography (CBCT) data for any non-circular trajectories.
Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast 3D Medical Imaging
To address this limitation, we propose a 3D Adaptive Masked Autoencoders (AMAE) architecture that accommodates a variable number of 3D input contrasts per subject.
Refusal Behavior in Large Language Models: A Nonlinear Perspective
Refusal behavior in large language models (LLMs) enables them to decline responding to harmful, unethical, or inappropriate prompts, ensuring alignment with ethical standards.
Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning
Calving front position variation of marine-terminating glaciers is an indicator of ice mass loss and a crucial parameter in numerical glacier models.
ProPLIKS: Probablistic 3D human body pose estimation
We present a novel approach for 3D human pose estimation by employing probabilistic modeling.
Gesture Classification in Artworks Using Contextual Image Features
Recognizing gestures in artworks can add a valuable dimension to art understanding and help to acknowledge the role of the sense of smell in cultural heritage.
Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields
Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses.
Probing for Consciousness in Machines
This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness.
Nonlinear Neural Dynamics and Classification Accuracy in Reservoir Computing
Reservoir computing - information processing based on untrained recurrent neural networks with random connections - is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixpoint dynamics of the network.
A Realistic Collimated X-Ray Image Simulation Pipeline
Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source.
Nuremberg Letterbooks: A Multi-Transcriptional Dataset of Early 15th Century Manuscripts for Document Analysis
Most datasets in the field of document analysis utilize highly standardized labels, which, while simplifying specific tasks, often produce outputs that are not directly applicable to humanities research.
Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging
Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases.
DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits
Traditional CBCT reconstruction methods for arbitrary orbits, like iterative reconstruction algorithms, are computationally expensive and memory-intensive.
Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data
To generalize our findings across languages and disorders, we validated our approach on a dataset of Spanish-speaking Parkinson's disease patients, leveraging pretrained models from healthy English-speaking datasets, and demonstrated that careful pretraining on large-scale task-specific datasets can maintain favorable accuracy under DP constraints.
Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data
Due to the limited size of flat-panel detectors, anatomical structures might be missing outside the limited field-of-view (FOV), which restricts the clinical applications of CBCT systems.
Enhancing Cross-Modality Synthesis: Subvolume Merging for MRI-to-CT Conversion
Providing more precise tissue attenuation information, synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) contributes to improved radiation therapy treatment planning.
Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models
The imitation of cursive handwriting is mainly limited to generating handwritten words or lines.
Generalist Segmentation Algorithm for Photoreceptors Analysis in Adaptive Optics Imaging
Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions.
Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology
Generating physician letters is a time-consuming task in daily clinical practice.
Recurrence Resonance -- Noise-Enhanced Dynamics in Recurrent Neural Networks
In specific motifs of three recurrently connected neurons with probabilistic response, the spontaneous information flux, defined as the mutual information between subsequent states, has been shown to increase by adding ongoing white noise of some optimal strength to each of the neurons \cite{krauss2019recurrence}.
UnPaSt: unsupervised patient stratification by differentially expressed biclusters in omics data
Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical approaches.
Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces
We show that a transfer-learning approach using weakly labeled training data can remarkably improve the classification of fragrant spaces and, more generally, artistic scene depictions.
Synthetic Electroretinogram Signal Generation Using Conditional Generative Adversarial Network for Enhancing Classification of Autism Spectrum Disorder
The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light.
Smell and Emotion: Recognising emotions in smell-related artworks
Emotions and smell are underrepresented in digital art history.
Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech
This paper introduces a novel combination of two tasks, previously treated separately: acoustic-to-articulatory speech inversion (AAI) and phoneme-to-articulatory (PTA) motion estimation.
Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives
Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, informed by an understanding of the measurement process.
Review of Zero-Shot and Few-Shot AI Algorithms in The Medical Domain
This integrated review of diverse papers on few-shot, zero-shot, and regular object detection reveals a shared focus on advancing the field through novel frameworks and techniques.
Towards Intelligent Speech Assistants in Operating Rooms: A Multimodal Model for Surgical Workflow Analysis
To develop intelligent speech assistants and integrate them seamlessly with intra-operative decision-support frameworks, accurate and efficient surgical phase recognition is a prerequisite.
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Attri-Net first counterfactually generates class-specific attribution maps to highlight the disease evidence, then performs classification with logistic regression classifiers based solely on the attribution maps.
On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation
This study analyzes the influence of a spline-based motion model within an existing rigid motion compensation algorithm for cone-beam CT on the recoverable motion frequencies.
SNOBERT: A Benchmark for clinical notes entity linking in the SNOMED CT clinical terminology
The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature.
Application of Gated Recurrent Units for CT Trajectory Optimization
The results show that the GRU-optimized scan trajectories can outperform traditional circular CT trajectories in terms of image quality metrics.
Analyzing Narrative Processing in Large Language Models (LLMs): Using GPT4 to test BERT
We found that the activation vectors of the hidden units cluster according to stylistic variations in earlier layers of BERT (1) than narrative content (4-5).
Self-Supervised Learning for Interventional Image Analytics: Towards Robust Device Trackers
An accurate detection and tracking of devices such as guiding catheters in live X-ray image acquisitions is an essential prerequisite for endovascular cardiac interventions.
Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images
Motion artifacts can compromise the diagnostic value of computed tomography (CT) images.
Reference-Free Multi-Modality Volume Registration of X-Ray Microscopy and Light-Sheet Fluorescence Microscopy
Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as two pivotal imaging tools in preclinical research on bone remodeling diseases, offering micrometer-level resolution.
Generating synthetic light-adapted 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.
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.
A 2D Sinogram-Based Approach to Defect Localization in Computed Tomography
We use a U-Net-based architecture for defect segmentation.
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 #21 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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
Bayesian U-Net for Segmenting Glaciers in SAR Imagery
It is therefore important to precisely monitor the position of the calving front.
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.
