259 research outputs found
Benchmarking Deep Learning Models for Tooth Structure Segmentation.
A wide range of deep learning (DL) architectures with varying depths are available, with developers usually choosing one or a few of them for their specific task in a nonsystematic way. Benchmarking (i.e., the systematic comparison of state-of-the art architectures on a specific task) may provide guidance in the model development process and may allow developers to make better decisions. However, comprehensive benchmarking has not been performed in dentistry yet. We aimed to benchmark a range of architecture designs for 1 specific, exemplary case: tooth structure segmentation on dental bitewing radiographs. We built 72 models for tooth structure (enamel, dentin, pulp, fillings, crowns) segmentation by combining 6 different DL network architectures (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network, Mask Attention Network) with 12 encoders from 3 different encoder families (ResNet, VGG, DenseNet) of varying depth (e.g., VGG13, VGG16, VGG19). On each model design, 3 initialization strategies (ImageNet, CheXpert, random initialization) were applied, resulting overall into 216 trained models, which were trained up to 200 epochs with the Adam optimizer (learning rate = 0.0001) and a batch size of 32. Our data set consisted of 1,625 human-annotated dental bitewing radiographs. We used a 5-fold cross-validation scheme and quantified model performances primarily by the F1-score. Initialization with ImageNet or CheXpert weights significantly outperformed random initialization (P < 0.05). Deeper and more complex models did not necessarily perform better than less complex alternatives. VGG-based models were more robust across model configurations, while more complex models (e.g., from the ResNet family) achieved peak performances. In conclusion, initializing models with pretrained weights may be recommended when training models for dental radiographic analysis. Less complex model architectures may be competitive alternatives if computational resources and training time are restricting factors. Models developed and found superior on nondental data sets may not show this behavior for dental domain-specific tasks
Understanding deep learning - challenges and prospects
The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. The current narrative review was planned to make comprehension of Artificial Intelligence algorithms relatively straightforward. The focus was planned to be kept on the current developments and prospects of Artificial Intelligence in dentistry, especially Deep Learning and Convolutional Neural Networks in diagnostic imaging. The narrative review may facilitate the interpretation of seemingly perplexing research published widely in dental journals
Pilates Pose Classification Using MediaPipe and Convolutional Neural Networks with Transfer Learning
A sedentary lifestyle can lead to heart disease, cancer, and type 2 diabetes. An anaerobic exercise called pilates can address these problems. Although pilates training can provide health benefits, the heavy load of pilates poses may cause severe muscle injury if not done properly. Surveys have found that many teenagers are unaware of the movements in pilates poses. Therefore, a system is needed to help users classify pilates poses accurately. MediaPipe is a system that accurately extracts the real time human body skeleton. Convolutional Neural Network (CNN) with transfer learning is an accurate method for image classification. There have been several studies investigated pilates poses classification. However, there is still no research applies the MediaPipe as a skeleton feature extractor and CNN with a transfer learning to classify pilates poses. In addition, previous research still does not implement the pilates poses classification in real-time. Based on this problem, this study creates a system using MediaPipe as a feature extractor and CNN with transfer learning as a real-time pilates poses classifier. This system runs on a mobile device and gets information from a camera sensor. The results from MediaPipe then be classified by pre-trained CNN architectures with transfer learning: MobileNetV2, Xception, and ResNet50. The best model was obtained by MobileNetV2, which had an f1 score of 98%. Ten people who didn't know much about Pilates also tested the system. They all agreed that the app could accurately identify Pilates poses, make people more interested in Pilates, and help them learn more about Pilates
Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition
Deep learning has been successfully applied to recognizing both natural
images and medical images. However, there remains a gap in recognizing 3D
neuroimaging data, especially for psychiatric diseases such as schizophrenia
and depression that have no visible alteration in specific slices. In this
study, we propose to process the 3D data by a 2+1D framework so that we can
exploit the powerful deep 2D Convolutional Neural Network (CNN) networks
pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition.
Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics (grey
matter, white matter, and cerebrospinal fluid) are decomposed to 2D slices
according to neighboring voxel positions and inputted to 2D CNN models
pre-trained on the ImageNet to extract feature maps from three views (axial,
coronal, and sagittal). Global pooling is applied to remove redundant
information as the activation patterns are sparsely distributed over feature
maps. Channel-wise and slice-wise convolutions are proposed to aggregate the
contextual information in the third view dimension unprocessed by the 2D CNN
model. Multi-metric and multi-view information are fused for final prediction.
Our approach outperforms handcrafted feature-based machine learning, deep
feature approach with a support vector machine (SVM) classifier and 3D CNN
models trained from scratch with better cross-validation results on publicly
available Northwestern University Schizophrenia Dataset and the results are
replicated on another independent dataset
MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis
There is no doubt that advanced artificial intelligence models and high
quality data are the keys to success in developing computational pathology
tools. Although the overall volume of pathology data keeps increasing, a lack
of quality data is a common issue when it comes to a specific task due to
several reasons including privacy and ethical issues with patient data. In this
work, we propose to exploit knowledge distillation, i.e., utilize the existing
model to learn a new, target model, to overcome such issues in computational
pathology. Specifically, we employ a student-teacher framework to learn a
target model from a pre-trained, teacher model without direct access to source
data and distill relevant knowledge via momentum contrastive learning with
multi-head attention mechanism, which provides consistent and context-aware
feature representations. This enables the target model to assimilate
informative representations of the teacher model while seamlessly adapting to
the unique nuances of the target data. The proposed method is rigorously
evaluated across different scenarios where the teacher model was trained on the
same, relevant, and irrelevant classification tasks with the target model.
Experimental results demonstrate the accuracy and robustness of our approach in
transferring knowledge to different domains and tasks, outperforming other
related methods. Moreover, the results provide a guideline on the learning
strategy for different types of tasks and scenarios in computational pathology.
Code is available at: \url{https://github.com/trinhvg/MoMA}.Comment: Preprin
Applying Transfer Learning in Classification of Ischemia from Myocardial Polar Maps in PET Cardiac Perfusion Imaging
Introduction: Ischemia is defined as the restriction of blood flow to a body organ, such as the heart, resulting in a cutback in oxygen supply. Myocardial ischemia is characterized by an imbalance between myocardial oxygen supply and demand, causing cardiac dysfunction, arrhythmia, myocardial infarction, and sudden death. Positron emission tomography myocardial perfusion imaging (PET-MPI) is an examination for accurately evaluating blood circulation to the heart muscle at stress and rest. Images obtained from this technique can be interpreted by experts or potentially classified by deep learning for the diagnosis of cardiac ischemia. Although deep learning has proved to be effective for medical image classification tasks, the challenge of small medical image datasets for model training remains to exist. Transfer learning is a state-of-the-art technique for resolving this challenge by utilizing pre-trained models for a new task. Pre-trained models are deep convolutional neural networks (CNNs) trained on a vast dataset, such as ImageNet, capable of transferring learned weights to a new classification problem. Objective: To study the effectiveness of image classification using transfer learning and benchmarking pre-trained CNN models for the classification of myocardial ischemia from myocardial polar maps in PET 15O-H2O cardiac perfusion imaging. Subject and methods: 138 JPEG polar maps from a 15O-H2O stress perfusion test from patients classified as ischemic or non-ischemic were used. Experiments for comparing a total of 20 pre-trained CNN models were performed. The results were compared against a custom CNN developed on the same dataset. Python programming language and its relevant libraries for deep learning were used. Results and discussion: Pre-trained models showed reliable performance compared to a custom-built CNN. VGG19, VGG16, DenseNet169, and Xception were superior among all pre-trained models. Ensemble learning improved overall performance, closest to the clinical interpretation level
Brain informed transfer learning for categorizing construction hazards
A transfer learning paradigm is proposed for "knowledge" transfer between the
human brain and convolutional neural network (CNN) for a construction hazard
categorization task. Participants' brain activities are recorded using
electroencephalogram (EEG) measurements when viewing the same images (target
dataset) as the CNN. The CNN is pretrained on the EEG data and then fine-tuned
on the construction scene images. The results reveal that the EEG-pretrained
CNN achieves a 9 % higher accuracy compared with a network with same
architecture but randomly initialized parameters on a three-class
classification task. Brain activity from the left frontal cortex exhibits the
highest performance gains, thus indicating high-level cognitive processing
during hazard recognition. This work is a step toward improving machine
learning algorithms by learning from human-brain signals recorded via a
commercially available brain-computer interface. More generalized visual
recognition systems can be effectively developed based on this approach of
"keep human in the loop"
- …