J Biomech Eng. 2024 Mar 26:1-22. doi: 10.1115/1.4065165. Online ahead of print.

ABSTRACT

PURPOSE: We present an unsupervised deep learning method to perform flow denoising and super-resolution without high resolution labels. We demonstrate the ability of this model to reconstruct 3D stenosis and aneurysm flows, with varying geometries, orientations, and boundary conditions.

METHODS: Ground truth data was generated using computational fluid dynamics, and then corrupted with multiplicative Gaussian noise. Autoencoders were used to compress the representations of the flow domain geometry and the (possibly noisy and low-resolution) flow field. These representations were used to condition a physics-informed neural network. A physics-based loss was implemented to train the model to recover lost information from the noisy input by transforming the flow to a solution of the Navier-Stokes equations.

RESULTS: Our experiments achieved mean squared errors in the true flow reconstruction of order 1.0e-4, and root mean squared residuals of order 1.0e-2 for the momentum and continuity equations. Our method yielded correlation coefficients of 0.971 for the pressure field and 0.82 for the wall shear stress magnitude field.

CONCLUSION: By performing point-wise predictions of the flow, the model was able to robustly denoise and super-resolve the field to 20x the input resolution.

PMID:38529728 | DOI:10.1115/1.4065165

bioRxiv [Preprint]. 2024 Mar 2:2024.02.27.582387. doi: 10.1101/2024.02.27.582387.

ABSTRACT

The recognition and binding of nucleic acids (NAs) by proteins depends upon complementary chemical, electrostatic and geometric properties of the protein-NA binding interface. Structural models of protein-NA complexes provide insights into these properties but are scarce relative to models of unbound proteins. We present a deep learning approach for predicting protein-NA binding given the apo structure of a protein (PNAbind). Our method utilizes graph neural networks to encode spatial distributions of physicochemical and geometric properties of the protein molecular surface that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein and can discriminate between specificity for DNA or RNA binding. We show that such predictions made on protein structures modeled with AlphaFold2 can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Using local encodings, our models predict the location of NA binding sites at the level of individual binding residues. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92-0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and show that our predictions are consistent with experimental RNA binding data.

PMID:38529493 | PMC:PMC10962711 | DOI:10.1101/2024.02.27.582387

Front Oncol. 2024 Mar 11;14:1360831. doi: 10.3389/fonc.2024.1360831. eCollection 2024.

ABSTRACT

BACKGROUND: Rapid On-Site Evaluation (ROSE) during flexible bronchoscopy (FB) can improve the adequacy of biopsy specimens and diagnostic yield of lung cancer. However, the lack of cytopathologists has restricted the wide use of ROSE.

OBJECTIVE: To develop a ROSE artificial intelligence (AI) system using deep learning techniques to differentiate malignant from benign lesions based on ROSE cytological images, and evaluate the clinical performance of the ROSE AI system.

METHOD: 6357 ROSE cytological images from 721 patients who underwent transbronchial biopsy were collected from January to July 2023 at the Tangdu Hospital, Air Force Medical University. A ROSE AI system, composed of a deep convolutional neural network (DCNN), was developed to identify whether there were malignant cells in the ROSE cytological images. Internal testing, external testing, and human-machine competition were used to evaluate the performance of the system.

RESULTS: The ROSE AI system identified images containing lung malignant cells with the accuracy of 92.97% and 90.26% on the internal testing dataset and external testing dataset respectively, and its performance was comparable to that of the experienced cytopathologist. The ROSE AI system also showed promising performance in diagnosing lung cancer based on ROSE cytological images, with accuracy of 89.61% and 87.59%, and sensitivity of 90.57% and 94.90% on the internal testing dataset and external testing dataset respectively. More specifically, the agreement between the ROSE AI system and the experienced cytopathologist in diagnosing common types of lung cancer, including squamous cell carcinoma, adenocarcinoma, and small cell lung cancer, demonstrated almost perfect consistency in both the internal testing dataset (κ = 0.930) and the external testing dataset (κ = 0.932).

CONCLUSIONS: The ROSE AI system demonstrated feasibility and robustness in identifying specimen adequacy, showing potential enhancement in the diagnostic yield of FB. Nevertheless, additional enhancements, incorporating a more diverse range of training data and leveraging advanced AI models with increased capabilities, along with rigorous validation through extensive multi-center randomized control assays, are crucial to guarantee the seamless and effective integration of this technology into clinical practice.

PMID:38529376 | PMC:PMC10961380 | DOI:10.3389/fonc.2024.1360831

Front Neurosci. 2024 Mar 11;18:1366165. doi: 10.3389/fnins.2024.1366165. eCollection 2024.

ABSTRACT

Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-to-susceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural networks with our approach, adaptive convolution, that learns the mapping between the additional presented information (acquisition parameters) and the changes in the phase images associated with these varying acquisition parameters. By associating a-priori information with the network parameters itself, the optimal set of convolution weights is selected based on data-specific attributes, leading to generalizability towards changes in acquisition parameters. Moreover, we demonstrate the feasibility of pre-training on synthetic data and transfer learning to clinical brain data to achieve substantial improvements in the computation of susceptibility maps. The adaptive convolution 3D U-Net demonstrated generalizability in acquisition parameters on synthetic and in-vivo data and outperformed models lacking adaptive convolution or transfer learning. Further experiments demonstrate the impact of the side information on the adaptive model and assessed susceptibility map computation on simulated pathologic data sets and measured phase data.

PMID:38529264 | PMC:PMC10962327 | DOI:10.3389/fnins.2024.1366165

ArXiv [Preprint]. 2024 Feb 14:arXiv:2402.09569v1.

ABSTRACT

Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD). However, manual assessment of CAC often requires radiological expertise, time, and invasive imaging techniques. The purpose of this multicenter study is to validate an automated cardiac plaque detection model using a 3D multiclass nnU-Net for gated and non-gated non-contrast chest CT volumes. CT scans were performed at three tertiary care hospitals and collected as three datasets, respectively. Heart, aorta, and lung segmentations were determined using TotalSegmentator, while plaques in the coronary arteries and heart valves were manually labeled for 801 volumes. In this work we demonstrate how the nnU-Net semantic segmentation pipeline may be adapted to detect plaques in the coronary arteries and valves. With a linear correction, nnU-Net deep learning methods may also accurately estimate Agatston scores on chest non-contrast CT scans. Compared to manual Agatson scoring, automated Agatston scoring indicated a slope of the linear regression of 0.841 with an intercept of +16 HU (R2 = 0.97). These results are an improvement over previous work assessing automated Agatston score computation in non-gated CT scans.

PMID:38529079 | PMC:PMC10962750

Front Plant Sci. 2024 Mar 11;15:1305358. doi: 10.3389/fpls.2024.1305358. eCollection 2024.

ABSTRACT

INTRODUCTION: Early detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases.

METHODS: To solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information.

RESULTS: The experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet.

DISCUSSION: The effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.

PMID:38529067 | PMC:PMC10961398 | DOI:10.3389/fpls.2024.1305358

Proc ACM Interact Mob Wearable Ubiquitous Technol. 2023 Mar;7(1):17. doi: 10.1145/3580804. Epub 2023 Mar 28.

ABSTRACT

End-to-end deep learning models are increasingly applied to safety-critical human activity recognition (HAR) applications, e.g., healthcare monitoring and smart home control, to reduce developer burden and increase the performance and robustness of prediction models. However, integrating HAR models in safety-critical applications requires trust, and recent approaches have aimed to balance the performance of deep learning models with explainable decision-making for complex activity recognition. Prior works have exploited the compositionality of complex HAR (i.e., higher-level activities composed of lower-level activities) to form models with symbolic interfaces, such as concept-bottleneck architectures, that facilitate inherently interpretable models. However, feature engineering for symbolic concepts-as well as the relationship between the concepts-requires precise annotation of lower-level activities by domain experts, usually with fixed time windows, all of which induce a heavy and error-prone workload on the domain expert. In this paper, we introduce X-CHAR , an eXplainable Complex Human Activity Recognition model that doesn't require precise annotation of low-level activities, offers explanations in the form of human-understandable, high-level concepts, while maintaining the robust performance of end-to-end deep learning models for time series data. X-CHAR learns to model complex activity recognition in the form of a sequence of concepts. For each classification, X-CHAR outputs a sequence of concepts and a counterfactual example as the explanation. We show that the sequence information of the concepts can be modeled using Connectionist Temporal Classification (CTC) loss without having accurate start and end times of low-level annotations in the training dataset-significantly reducing developer burden. We evaluate our model on several complex activity datasets and demonstrate that our model offers explanations without compromising the prediction accuracy in comparison to baseline models. Finally, we conducted a mechanical Turk study to show that the explanations provided by our model are more understandable than the explanations from existing methods for complex activity recognition.

PMID:38529008 | PMC:PMC10961595 | DOI:10.1145/3580804

Open Mind (Camb). 2024 Mar 13;8:235-264. doi: 10.1162/opmi_a_00126. eCollection 2024.

ABSTRACT

The dynamics of the mind are complex. Mental processes unfold continuously in time and may be sensitive to a myriad of interacting variables, especially in naturalistic settings. But statistical models used to analyze data from cognitive experiments often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to simulations of dynamical cognitive processes, including speech comprehension, visual perception, and goal-directed behavior. But due to poor interpretability, deep learning is generally not used for scientific analysis. Here, we bridge this gap by showing that deep learning can be used, not just to imitate, but to analyze complex processes, providing flexible function approximation while preserving interpretability. To do so, we define and implement a nonlinear regression model in which the probability distribution over the response variable is parameterized by convolving the history of predictors over time using an artificial neural network, thereby allowing the shape and continuous temporal extent of effects to be inferred directly from time series data. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many cognitive processes and may critically affect the interpretation of data. We demonstrate substantial improvements on behavioral and neuroimaging data from the language processing domain, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions in cognitive (neuro)science that are otherwise hard to study.

PMID:38528907 | PMC:PMC10962694 | DOI:10.1162/opmi_a_00126

J Big Data. 2024;11(1):43. doi: 10.1186/s40537-024-00898-6. Epub 2024 Mar 23.

ABSTRACT

Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information. A popular recent method, mixup, uses convex combinations of pairs of original samples to generate new samples. However, as we show in our experiments, mixup can produce undesirable synthetic samples, where the data is sampled off the manifold and can contain incorrect labels. We propose ζ-mixup, a generalization of mixup with provably and demonstrably desirable properties that allows convex combinations of T≥2 samples, leading to more realistic and diverse outputs that incorporate information from T original samples by using a p-series interpolant. We show that, compared to mixup, ζ-mixup better preserves the intrinsic dimensionality of the original datasets, which is a desirable property for training generalizable models. Furthermore, we show that our implementation of ζ-mixup is faster than mixup, and extensive evaluation on controlled synthetic and 26 diverse real-world natural and medical image classification datasets shows that ζ-mixup outperforms mixup, CutMix, and traditional data augmentation techniques. The code will be released at https://github.com/kakumarabhishek/zeta-mixup.

PMID:38528850 | PMC:PMC10960781 | DOI:10.1186/s40537-024-00898-6

RNA Biol. 2024 Jan;21(1):1-12. doi: 10.1080/15476286.2024.2329451. Epub 2024 Mar 25.

ABSTRACT

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.

PMID:38528797 | DOI:10.1080/15476286.2024.2329451

Korean J Radiol. 2024 Apr;25(4):395-398. doi: 10.3348/kjr.2024.0108.

NO ABSTRACT

PMID:38528697 | DOI:10.3348/kjr.2024.0108

J Neurointerv Surg. 2024 Mar 25:jnis-2023-021154. doi: 10.1136/jnis-2023-021154. Online ahead of print.

ABSTRACT

BACKGROUND: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).

METHODS: Deep learning models were trained and tested on baseline clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in stroke patients who underwent EVT. Classical machine learning models (logistic regression and random forest classifiers) were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and classical machine learning models.

RESULTS: A total of 975 patients (550 men; mean±SD age 67.5±15.1 years) were studied with 778 patients in the model development cohort and 197 in the external validation cohort. The deep learning model trained on baseline CT and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 vs 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT head, and CT angiography) and MR PREDICTS models (all Q<0.05).

CONCLUSIONS: The discriminative performance of deep learning for predicting functional independence was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.

PMID:38527795 | DOI:10.1136/jnis-2023-021154

Eur Heart J. 2024 Mar 25:ehad775. doi: 10.1093/eurheartj/ehad775. Online ahead of print.

NO ABSTRACT

PMID:38527415 | DOI:10.1093/eurheartj/ehad775

Spectrochim Acta A Mol Biomol Spectrosc. 2024 Mar 22;314:124181. doi: 10.1016/j.saa.2024.124181. Online ahead of print.

ABSTRACT

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

PMID:38527410 | DOI:10.1016/j.saa.2024.124181

Food Chem. 2024 Mar 21;448:139078. doi: 10.1016/j.foodchem.2024.139078. Online ahead of print.

ABSTRACT

A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 ∼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 ∼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.

PMID:38527403 | DOI:10.1016/j.foodchem.2024.139078

Phys Med Biol. 2024 Mar 25. doi: 10.1088/1361-6560/ad3797. Online ahead of print.

ABSTRACT

Task-adapted image reconstruction methods using end-to-end trainable&#xD;neural networks (NNs) have been proposed to optimize reconstruction for subsequent&#xD;processing tasks, such as segmentation. However, their training typically requires&#xD;considerable hardware resources and thus, only relatively simple building blocks,&#xD;e.g. U-Nets, are typically used, which, albeit powerful, do not integrate model-&#xD;specific knowledge. In this work, we extend an end-to-end trainable task-adapted&#xD;image reconstruction method for a clinically realistic reconstruction and segmentation&#xD;problem of bone and cartilage in 3D knee MRI by incorporating statistical shape models&#xD;(SSMs). The SSMs model the prior information and help to regularize the segmentation&#xD;maps as a final post-processing step. We compare the proposed method to a &#xD;simultaneous multitask learning approach for image reconstruction and&#xD;segmentation (MTL) and to a complex SSMs-informed segmentation pipeline (SIS).&#xD;Our experiments show that the combination of joint end-to-end training and SSMs&#xD;to further regularize the segmentation maps obtained by MTL highly improves the&#xD;results, especially in terms of mean and maximal surface errors. In particular, we&#xD;achieve the segmentation quality of SIS and, at the same time, a substantial model&#xD;reduction that yields a five-fold decimation in model parameters and a computational&#xD;speedup of an order of magnitude. Remarkably, even for undersampling factors of up to&#xD;R = 8, the obtained segmentation maps are of comparable quality to those obtained&#xD;by SIS from ground-truth images.

PMID:38527376 | DOI:10.1088/1361-6560/ad3797

Singapore Med J. 2024 Mar 1;65(3):133-140. doi: 10.4103/singaporemedj.SMJ-2023-187. Epub 2024 Mar 26.

ABSTRACT

INTRODUCTION: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities.

METHODS: We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively. We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. We then combined the open-source Kvasir-Capsule dataset (n = 43) and locally collected CE data (n = 29).

RESULTS: Model performance was compared using averaged five-fold and two-fold cross-validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200-250 images per second and showed good discrimination on time-critical abnormalities such as bleeding.

CONCLUSION: Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints.

PMID:38527297 | DOI:10.4103/singaporemedj.SMJ-2023-187

Ann Intern Med. 2024 Mar 26. doi: 10.7326/M23-1898. Online ahead of print.

ABSTRACT

BACKGROUND: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable.

OBJECTIVE: To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility.

DESIGN: Risk prediction study.

SETTING: Outpatients potentially eligible for primary cardiovascular prevention.

PARTICIPANTS: The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated.

MEASUREMENTS: 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score.

RESULTS: Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]).

LIMITATION: Retrospective study design using electronic medical records.

CONCLUSION: On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data.

PRIMARY FUNDING SOURCE: None.

PMID:38527287 | DOI:10.7326/M23-1898

PLoS One. 2024 Mar 25;19(3):e0301174. doi: 10.1371/journal.pone.0301174. eCollection 2024.

ABSTRACT

Crop losses caused by diseases and pests present substantial challenges to global agriculture, with groundnut crops particularly vulnerable to their detrimental effects. This study introduces the Groundnut Vision Transformer (GNViT) model, a novel approach that harnesses a pre-trained Vision Transformer (ViT) on the ImageNet dataset. The primary goal is to detect and classify various pests affecting groundnut crops. Rigorous training and evaluation were conducted using a comprehensive dataset from IP102, encompassing pests such as Thrips, Aphids, Armyworms, and Wireworms. The GNViT model's effectiveness was assessed using reliability metrics, including the F1-score, recall, and overall accuracy. Data augmentation with GNViT resulted in a significant increase in training accuracy, achieving 99.52%. Comparative analysis highlighted the GNViT model's superior performance, particularly in accuracy, compared to state-of-the-art methodologies. These findings underscore the potential of deep learning models, such as GNViT, in providing reliable pest classification solutions for groundnut crops. The deployment of advanced technological solutions brings us closer to the overarching goal of reducing crop losses and enhancing global food security for the growing population.

PMID:38527074 | DOI:10.1371/journal.pone.0301174

Neuroinformatics. 2024 Mar 25. doi: 10.1007/s12021-024-09655-9. Online ahead of print.

ABSTRACT

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

PMID:38526701 | DOI:10.1007/s12021-024-09655-9