BMC Bioinformatics. 2024 Jan 24;25(1):41. doi: 10.1186/s12859-024-05656-2.

ABSTRACT

BACKGROUND: With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed.

RESULTS: Here, we present inClust+, a deep generative framework for the multi-omics. It's built on previous inClust that is specific for transcriptome data, and augmented with two mask modules designed for multimodal data processing: an input-mask module in front of the encoder and an output-mask module behind the decoder. InClust+ was first used to integrate scRNA-seq and MERFISH data from similar cell populations, and to impute MERFISH data based on scRNA-seq data. Then, inClust+ was shown to have the capability to integrate the multimodal data (e.g. tri-modal data with gene expression, chromatin accessibility and protein abundance) with batch effect. Finally, inClust+ was used to integrate an unlabeled monomodal scRNA-seq dataset and two labeled multimodal CITE-seq datasets, transfer labels from CITE-seq datasets to scRNA-seq dataset, and generate the missing modality of protein abundance in monomodal scRNA-seq data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools in the corresponding task.

CONCLUSIONS: The inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

PMID:38267858 | DOI:10.1186/s12859-024-05656-2

Eur J Nucl Med Mol Imaging. 2024 Jan 25. doi: 10.1007/s00259-024-06618-9. Online ahead of print.

ABSTRACT

PURPOSE: Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study.

METHODS: We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions).

RESULTS: The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses.

CONCLUSION: A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.

PMID:38267686 | DOI:10.1007/s00259-024-06618-9

Sci Rep. 2024 Jan 25;14(1):2126. doi: 10.1038/s41598-024-51765-6.

ABSTRACT

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and copy number variations (CNVs) correlated with IDH1 mutation status were selected as potential inputs to train artificial neural networks (ANNs) to predict IDH1 mutation status. Grade 2 and 3 glioma cases from the Memorial Sloan Kettering dataset (n = 404) and Grade 3 glioma cases with subtotal resection (STR) from Northwestern University (NU) (n = 21) were used to further evaluate the best performing ANN model as independent datasets. IDH1 mutation is associated with decreased CNVs of EGFR (21% vs. 3%), CDKN2A (20% vs. 6%), PTEN (14% vs. 1.7%), and increased percentage of mutations for TP53 (15% vs. 63%), and ATRX (10% vs. 54%), which were all statistically significant (p < 0.001). Age > 40 was unable to identify high-risk IDH1-mutant with early progression. A glioma early progression risk prediction (GlioPredictor) score generated from the best performing ANN model (6/6/6/6/2/1) with 6 inputs, including CNVs of EGFR, PTEN and CDKN2A, mutation status of TP53 and ATRX, patient's age can predict IDH1 mutation status with over 90% accuracy. The GlioPredictor score identified a subgroup of high-risk IDH1-mutant in TCGA and NU datasets with early disease progression (p = 0.0019, 0.0238, respectively). The GlioPredictor that integrates age at diagnosis, CNVs of EGFR, CDKN2A, PTEN and mutation status of TP53, and ATRX can identify a small cohort of IDH-mutant with high risk of early progression. The current version of GlioPredictor mainly incorporated clinically often tested genetic biomarkers. Considering complexity of clinical and genetic features that correlate with glioma progression, future derivatives of GlioPredictor incorporating more inputs can be a potential supplement for adjuvant radiotherapy patient selection of IDH-mutant glioma patients.

PMID:38267516 | DOI:10.1038/s41598-024-51765-6

Ultrasound Med Biol. 2024 Jan 23:S0301-5629(23)00406-4. doi: 10.1016/j.ultrasmedbio.2023.12.011. Online ahead of print.

ABSTRACT

OBJECTIVE: The main objective of this study was to build a rich and high-quality thyroid ultrasound image database (TUD) for computer-aided diagnosis (CAD) systems to support accurate diagnosis and prognostic modeling of thyroid disorders. Because most of the raw thyroid ultrasound images contain artificial markers, which seriously affect the robustness of CAD systems because of their strong prior location information, we propose a marker mask inpainting (MMI) method to erase artificial markers and improve image quality.

METHODS: First, a set of thyroid ultrasound images were collected from the General Hospital of the Northern Theater Command. Then, two modules were designed in MMI, namely, the marker detection (MD) module and marker erasure (ME) module. The MD module detects all markers in the image and stores them in a binary mask. According to the binary mask, the ME module erases the markers and generates an unmarked image. Finally, a new TUD based on the marked images and unmarked images was built. The TUD is carefully annotated and statistically analyzed by professional physicians to ensure accuracy and consistency. Moreover, several normal thyroid gland images and some ancillary information on benign and malignant nodules are provided.

RESULTS: Several typical segmentation models were evaluated on the TUD. The experimental results revealed that our TUD can facilitate the development of more accurate CAD systems for the analysis of thyroid nodule-related lesions in ultrasound images. The effectiveness of our MMI method was determined in quantitative experiments.

CONCLUSION: The rich and high-quality resource TUD promotes the development of more effective diagnostic and treatment methods for thyroid diseases. Furthermore, MMI for erasing artificial markers and generating unmarked images is proposed to improve the quality of thyroid ultrasound images. Our TUD database is available at https://github.com/NEU-LX/TUD-Datebase.

PMID:38267314 | DOI:10.1016/j.ultrasmedbio.2023.12.011

Brief Funct Genomics. 2024 Jan 24:elae001. doi: 10.1093/bfgp/elae001. Online ahead of print.

ABSTRACT

Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for predicting protein methylation sites, but these techniques are time-consuming and costly. The ability to predict methylation sites using in silico techniques may help researchers identify potential candidate sites for future examination and make it easier to carry out site-specific investigations and downstream characterizations. In this research, we proposed a novel deep learning-based predictor, named DeepPRMS, to identify protein methylation sites in primary sequences. The DeepPRMS utilizes the gated recurrent unit (GRU) and convolutional neural network (CNN) algorithms to extract the sequential and spatial information from the primary sequences. GRU is used to extract sequential information, while CNN is used for spatial information. We combined the latent representation of GRU and CNN models to have a better interaction among them. Based on the independent test data set, DeepPRMS obtained an accuracy of 85.32%, a specificity of 84.94%, Matthew's correlation coefficient of 0.71 and a sensitivity of 85.80%. The results indicate that DeepPRMS can predict protein methylation sites with high accuracy and outperform the state-of-the-art models. The DeepPRMS is expected to effectively guide future research experiments for identifying potential methylated protein sites. The web server is available at http://deepprms.nitsri.ac.in/.

PMID:38267081 | DOI:10.1093/bfgp/elae001

Physiol Meas. 2024 Jan 24. doi: 10.1088/1361-6579/ad2217. Online ahead of print.

ABSTRACT

OBJECTIVE: Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are crucial in reducing its fatality rate. Currently, electrocardiography (ECG) serves as the primary tool for clinical diagnosis. However, detecting MI accurately through ECG remains challenging due to the complex and subtle pathological ECG changes it causes. To enhance the accuracy of ECG in detecting MI, a more thorough exploration of ECG signals is necessary to extract significant features.

APPROACH: In this paper, we propose an interpretable shapelet-based approach for MI detection using dynamic learning and deep learning. Firstly, the intrinsic dynamics of ECG signals are learned through dynamic learning. Then, a deep neural network is utilized to extract and select shapelets from ECG dynamics, which can capture locally specific ECG changes, and serve as discriminative features for identifying MI patients. Finally, the ensemble model for MI detection is built by integrating shapelets of multi-dimensional ECG dynamic signals.

MAIN RESULTS: The performance of the proposed method is evaluated on the public PTB dataset with accuracy, sensitivity, and specificity of 94.11%, 94.97%, and 90.98%.

SIGNIFICANCE: The shapelets obtained in this study exhibit significant morphological differences between MI and healthy subjects.

PMID:38266290 | DOI:10.1088/1361-6579/ad2217

PLoS One. 2024 Jan 24;19(1):e0295801. doi: 10.1371/journal.pone.0295801. eCollection 2024.

ABSTRACT

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.

PMID:38266011 | DOI:10.1371/journal.pone.0295801

J Chem Inf Model. 2024 Jan 24. doi: 10.1021/acs.jcim.3c01562. Online ahead of print.

ABSTRACT

Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in holistic and efficient tools specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative-deep-learning-enabled molecular generation and automated machine-learning-based antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole-cell-based antibiotic screening and design. It is open-source, thus allowing for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with a Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/10097899).

PMID:38265916 | DOI:10.1021/acs.jcim.3c01562

IEEE Trans Med Imaging. 2024 Jan 24;PP. doi: 10.1109/TMI.2024.3357986. Online ahead of print.

ABSTRACT

Chest radiography is the most common radiology examination for thoracic disease diagnosis, such as pneumonia. A tremendous number of chest X-rays prompt data-driven deep learning models in constructing computer-aided diagnosis systems for thoracic diseases. However, in realistic radiology practice, a deep learning-based model often suffers from performance degradation when trained on data with noisy labels possibly caused by different types of annotation biases. To this end, we present a novel stochastic neural ensemble learning (SNEL) framework for robust thoracic disease diagnosis using chest X-rays. The core idea of our method is to learn from noisy labels by constructing model ensembles and designing noise-robust loss functions. Specifically, we propose a fast neural ensemble method that collects parameters simultaneously across model instances and along optimization trajectories. Moreover, we propose a loss function that both optimizes a robust measure and characterizes a diversity measure of ensembles. We evaluated our proposed SNEL method on three publicly available hospital-scale chest X-ray datasets. The experimental results indicate that our method outperforms competing methods and demonstrate the effectiveness and robustness of our method in learning from noisy labels. Our code is available at https://github.com/hywang01/SNEL.

PMID:38265913 | DOI:10.1109/TMI.2024.3357986

IEEE Rev Biomed Eng. 2024 Jan 24;PP. doi: 10.1109/RBME.2024.3357877. Online ahead of print.

ABSTRACT

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

PMID:38265911 | DOI:10.1109/RBME.2024.3357877

IEEE Trans Pattern Anal Mach Intell. 2024 Jan 24;PP. doi: 10.1109/TPAMI.2024.3358168. Online ahead of print.

ABSTRACT

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

PMID:38265904 | DOI:10.1109/TPAMI.2024.3358168

IEEE J Biomed Health Inform. 2024 Jan 24;PP. doi: 10.1109/JBHI.2024.3357947. Online ahead of print.

ABSTRACT

Deep learning (DL) models have achieved remarkable success in various domains. But training an accurate DL model requires large amounts of data, which can be challenging to obtain in medical settings due to privacy concerns. Recently, federated learning (FL) has emerged as a promising solution that shares local models instead of raw data. However, FL in medical settings faces challenges of client drift due to the data heterogeneity across dispersed institutions. Although there exist studies to address this challenge, they mainly focus on the classification tasks that learn global representation of an entire image. Few have been studied on the dense prediction tasks, such as object detection. In this study, we propose dense contrastive-based federated learning (DCFL) tailored for dense prediction tasks in FL settings. DCFL introduces dense contrastive learning to FL, which aligns the local optimization objectives towards the global objective by maximizing the agreement of representations between the global and local models. Moreover, to improve the performance of dense target prediction at each level, DCFL applies multi-scale contrastive representation by utilizing multi-scale representations with dense features in contrastive learning. We evaluated DCFL on a set of realistic datasets for pulmonary nodule detection. DCFL demonstrates an overall performance improvement compared with the other federated learning methods in heterogeneous settings-improving the mean average precision by 4.13% and testing recall by 6.07% in highly heterogeneous settings.

PMID:38265899 | DOI:10.1109/JBHI.2024.3357947

IEEE J Biomed Health Inform. 2024 Jan 24;PP. doi: 10.1109/JBHI.2024.3357979. Online ahead of print.

ABSTRACT

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

PMID:38265898 | DOI:10.1109/JBHI.2024.3357979

Invest Ophthalmol Vis Sci. 2024 Jan 2;65(1):41. doi: 10.1167/iovs.65.1.41.

ABSTRACT

PURPOSE: To characterize the clinical effects of two RP1L1 hotspots in patients with East Asian occult macular dystrophy (OMD).

METHODS: Fifty-one patients diagnosed with OMD harboring monoallelic pathogenic RP1L1 variants (Miyake disease) from Japan, South Korea, and China were enrolled. Patients were classified into two genotype groups: group A, p.R45W, and group B, missense variants located between amino acids (aa) 1196 and 1201. The clinical parameters of the two genotypes were compared, and deep learning based on spectral-domain optical coherence tomographic (SD-OCT) images was used to distinguish the morphologic differences.

RESULTS: Groups A and B included 29 and 22 patients, respectively. The median age of onset in groups A and B was 14.0 and 40.0 years, respectively. The median logMAR visual acuity of groups A and B was 0.70 and 0.51, respectively, and the survival curve analysis revealed a 15-year difference in vision loss (logMAR 0.22). A statistically significant difference was observed in the visual field classification, but no significant difference was found in the multifocal electroretinographic classification. High accuracy (75.4%) was achieved in classifying genotype groups based on SD-OCT images using machine learning.

CONCLUSIONS: Distinct clinical severities and morphologic phenotypes supported by artificial intelligence-based classification were derived from the two investigated RP1L1 hotspots: a more severe phenotype (p.R45W) and a milder phenotype (1196-1201 aa). This newly identified genotype-phenotype association will be valuable for medical care and the design of therapeutic trials.

PMID:38265784 | DOI:10.1167/iovs.65.1.41

Curr Med Chem. 2024 Jan 22. doi: 10.2174/0109298673264218231121104407. Online ahead of print.

ABSTRACT

BACKGROUND: Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are time-- consuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets.

METHODS: In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance.

RESULTS: We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development.

CONCLUSION: Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

PMID:38265399 | DOI:10.2174/0109298673264218231121104407

Rev Sci Instrum. 2024 Jan 1;95(1):013004. doi: 10.1063/5.0180938.

ABSTRACT

We develop a modular and compactified optical circuit for the generation of optical beams for cooling, imaging, and controlling ultracold atoms. One of the simplifications that is made in our circuit is to admix the repumping beams to each other optical beams in its dedicated single-mode fiber. We implement our design, characterize the output, and show that the optical power efficiency of the circuit is in the region of 97%, and after fiber coupling, the efficiencies are in the range of 62-85%. Given its compact design and controllable optical sources, this setup should be adaptable to a variety of quantum experiments based on ultracold gases.

PMID:38265277 | DOI:10.1063/5.0180938

Br J Radiol. 2024 Jan 23:tqae006. doi: 10.1093/bjr/tqae006. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data.

METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127,030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present.

RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P=.02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps<.01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P=.19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P=.26), consolidation (100% [54/54] vs. 96.3% [52/54]; P=.50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P=.69).

CONCLUSION: SR-reconstructed chest radiographs using 64-fold reduced data showed lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities.

ADVANCE IN KNOWLEDGE: This is the first study applying super resolution in data reduction of chest radiographs.

PMID:38265235 | DOI:10.1093/bjr/tqae006

Pest Manag Sci. 2024 Jan 24. doi: 10.1002/ps.7959. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi-supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub-images containing weeds from those containing only turfgrass.

RESULTS: Compared with ResNet50, the evaluated SSL methods, Π-model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated.

CONCLUSION: These results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time- and labor-efficient than the FSL method. © 2024 Society of Chemical Industry.

PMID:38265105 | DOI:10.1002/ps.7959

J Cell Sci. 2024 Jan 24:jcs.261274. doi: 10.1242/jcs.261274. Online ahead of print.

ABSTRACT

Filopodia are slender, actin-filled membrane projections used by various cell types for environment exploration. Analyzing filopodia often involves visualizing them using actin, filopodia tip, or membrane markers. Due to the diversity of cell types that extend filopodia, from amoeboid to mammalian, it can be challenging for some to find a reliable filopodia analysis workflow suited for their cell type and preferred visualization method. The lack of an automated workflow capable of analyzing amoeboid filopodia with only a filopodia tip label prompted the development of filoVision. filoVision is an adaptable deep learning platform featuring filoTips and filoSkeleton. filoTips uses a single tip marker to label filopodia tips and the cytosol, allowing information extraction without actin or membrane markers. In contrast, filoSkeleton combines a tip marker with actin labeling for a more comprehensive analysis of filopodia shafts in addition to tip protein analysis. The ZeroCostDL4Mic deep learning framework facilitates accessibility and customization for different datasets and cell types, making filoVision a flexible tool for automated analysis of tip-marked filopodia across various cell types and user data.

PMID:38264939 | DOI:10.1242/jcs.261274

Front Oncol. 2024 Jan 9;13:1218897. doi: 10.3389/fonc.2023.1218897. eCollection 2023.

ABSTRACT

PURPOSE: No multi-center radiomics models have been built to predict delayed remission (DR) after transsphenoidal surgery (TSS) in Cushing's disease (CD). The present study aims to build clinical and radiomics models based on data from three centers to predict DR after TSS in CD.

METHODS: A total of 122 CD patients from Peking Union Medical College Hospital, Xuanwu Hospital, and Fuzhou General Hospital were enrolled between January 2000 and January 2019. The T1-weighted gadolinium-enhanced MRI images and clinical data were used as inputs to build clinical and radiomics models. The regions of interest (ROI) of MRI images were automatically defined by a deep learning algorithm developed by our team. The area under the curve (AUC) of receiver operating characteristic (ROC) curves was used to evaluate the performance of the models. In total, 10 machine learning algorithms were used to construct models.

RESULTS: The overall DR rate is 44.3% (54/122). According to multivariate Logistic regression analysis, patients with higher BMI and lower postoperative cortisol levels are more likely to achieve a higher rate of delayed remission. Among the 10 models, XGBoost achieved the best performance among all models in both clinical and radiomics models with AUC values of 0.767 and 0.819 respectively. The results from SHAP value and LIME algorithms revealed that postoperative cortisol level (PoC) and BMI were the most important features associated with DR.

CONCLUSION: Radiomics models can be built as an effective noninvasive method to predict DR and might be useful in assisting neurosurgeons in making therapeutic plans after TSS for CD patients. These results are preliminary and further validation in a larger patient sample is needed.

PMID:38264759 | PMC:PMC10803608 | DOI:10.3389/fonc.2023.1218897