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Found 64 papers, 21 papers with code
Attenuation artifact detection and severity classification in intracoronary OCT using mixed image representations
To enable automatic detection of the attenuation artifacts caused by blood residues and gas bubbles based on their severity, we propose a convolutional neural network that performs classification of the attenuation lines (A-lines) into three classes: no artifact, mild artifact and severe artifact.
Artificial Intelligence to Assess Dental Findings from Panoramic Radiographs -- A Multinational Study
The AI system demonstrated comparable or superior performance to human readers, particularly +67. 9% (95% CI: 54. 0%-81. 9%; p < . 001) sensitivity for identifying periapical radiolucencies and +4. 7% (95% CI: 1. 4%-8. 0%; p = . 008) sensitivity for identifying missing teeth.
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
To address this gap, we propose a living review that continuously tracks public datasets and their associated research artifacts across multiple medical imaging applications.
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures.
Nodule detection and generation on chest X-rays: NODE21 Challenge
To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays.
Transfer learning from a sparsely annotated dataset of 3D medical images
A base segmentation model (3D U-Net) was trained on a large and sparsely annotated dataset; its weights were used for transfer learning on four new down-stream segmentation tasks for which a fully annotated dataset was available.
Kidney abnormality segmentation in thorax-abdomen CT scans
Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities.
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Manual analysis of emphysema subtypes and severity is laborious and subjective.
Lumbar spine segmentation in MR images: a dataset and a public benchmark
This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography
Deep learning classification models for medical image analysis often perform well on data from scanners that were used during training.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
Domain adaptation strategies for cancer-independent detection of lymph node metastases
Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Structure and position-aware graph neural network for airway labeling
The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks (CNN) and enriched using graph neural networks.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow
In this work, we propose an uncertainty slice sampling (USS) strategy for semantic segmentation of 3D medical volumes that selects 2D image slices for annotation and compare it with various other strategies.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Deep Clustering Activation Maps for Emphysema Subtyping
We propose a deep learning clustering method that exploits dense features from a segmentation network for emphysema subtyping from computed tomography (CT) scans.
Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients
Therefore, we propose a weakly-supervised segmentation method based on dense regression activation maps (dRAMs).
Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning
The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19
The data includes results from 27 laboratory tests and a chest x-ray scored by a deep learning model.
Deep Learning for Chest X-ray Analysis: A Survey
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks.
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation
We utilize synthetic images generated with our method for data augmentation in cerebral microbleeds detection.
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods.
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI
Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions.
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study
This paper identifies a variety of components that increase the performance of CNN-based algorithms for COVID-19 grading from CT images.
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD.
Random smooth gray value transformations for cross modality learning with gray value invariant networks
Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples.
Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image.
Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Images
We show that the augmented visual evidence of the predictions highlights the biomarkers considered by experts for diagnosis and improves the final localization performance.
BIAS: Transparent reporting of biomedical image analysis challenges
The number of biomedical image analysis challenges organized per year is steadily increasing.
Function Follows Form: Regression from Complete Thoracic Computed Tomography Scans
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality.
mlVIRNET: Multilevel Variational Image Registration Network
We show that the use of a deep learning multilevel approach leads to significantly better registration results.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history
Participants: 409 CFIs of 238 eyes with GA from the Rotterdam Study (RS) and the Blue Mountain Eye Study (BMES) for model development, and 5, 379 CFIs of 625 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate.
Automated Gleason Grading of Prostate Biopsies using Deep Learning
We developed a fully automated deep learning system to grade prostate biopsies.
FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis
The method is tested using a held-out test dataset of 21, 176 chest x-rays (in-distribution) and a set of 14, 821 out-of-distribution x-ray images of incorrect orientation or anatomy.
Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration
Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v. 1. 3. 0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in color fundus (CF) images on a dataset with mixed presence of eye diseases.
Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obtain a radiological reference standard.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Context Encoding Chest X-rays
Chest X-rays are one of the most commonly used technologies for medical diagnosis.
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.
GANs for Medical Image Analysis
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification.
Chest X-ray Inpainting with Deep Generative Models
Generative adversarial networks have been successfully applied to inpainting in natural images.
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
The H&E slides were co-registered to the IHC slides.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities.
Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR
To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation.
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area.
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology.
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
The importance of stain normalization in colorectal tissue classification with convolutional networks
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set.
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy.
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.
