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13 results for “Deep Learning Classifier”
Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.
<p>Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. <em>Ecological Informatics</em>. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256</p> <p> </p> <p>Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location.</p> <p>To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database.</p> <p>The dataset contains songs of <strong>22 bird species</strong> from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was <strong>manually annotated </strong>by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from <strong>967 file recordings</strong>. A precise description of the distribution by species and country can be found in the associated article.</p> <p> </p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github</p> <p>The files were divided into a <strong>training folder</strong> and a<strong> validation folder</strong> to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation.</p> <p><strong>Process to select the species</strong> : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.</p> <p> </p> <p> </p> <p> </p>
Classifying Handedness in Chiral Nanomaterials Using Label Noise-Robust Deep Learning
<p>Images of individual Te chiral nanoparticles and labels of their handedness for classifier training.</p>
Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</p>
Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified
<p>Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.</p>
Deep learning based on convolutional neural networks to classify nanobiomechanical data
Open the record for dataset details and reuse information.
Developing Deep Learning Approaches to Find and Classify Architectural Design Decisions in Issue Tracking Systems
<p>This upload contains three files:</p> <ol> <li>mongodump-JiraRepos_2023-03-07-16 00.archive: Archive containing the issue data pulled from the Jira API.</li> <li>mongodump-MiningDesignDecisions.archive: Archive containing the data of our deep learning models and the labelled issues.</li> <li>mongodump-MiningDesignDecisions-lite.archive: Similar to the archive above, except this one only contains the best trained model (BERT). Also, it does not contain any embeddings or other files.</li> </ol> <p>This archive contains the data of our deep learning models and the labelled issues (MiningDesignDecisions archive).</p>
DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes
Open the record for dataset details and reuse information.
Deep Learning in Classifying Bowel Obstruction Radiographs
ClinicalTrials.gov study NCT06321614. IPD Sharing: NO. Countries: 1. Publications: 9.
ALS molecular subtypes are a combination of cellular and pathological features learned by deep multiomics classifiers (postmortem spinal cord)
GEO Series GSE272626. Homo sapiens. 266 samples. Type: Expression profiling by high throughput sequencing.
ALS molecular subtypes are a combination of cellular and pathological features learned by deep multiomics classifiers (postmortem cortex)
GEO Series GSE272624. Homo sapiens. 289 samples. Type: Expression profiling by high throughput sequencing.
Large-Population based Deep Learning Models in Classifying Primary Bone Tumors and Bone Infections based on Radiographs: a Retrospective and Multi-reader Multi-center Study
<p>This retrospective multicenter study collected patients via consecutive sampling between 2013 and 2022 from two cohorts: training cohort (from the Second Xiangya Hospital of Central South University) and testing cohort (from Xiangya Hospital of Central South University and Hunan Children's Hospital of Central South University). These lesions were identified to have bone involvement through pre-operative radiographs and were histologically diagnosed following biopsy or surgery. <br>(i) For the inclusion criteria, lesions were confirmed and diagnosed as PBTs according to the 2020 World Health Organization (WHO) system for the classification for tumors of bone 1 while bone infections were confirmed and proven by histology and (or) bacterial culture. The other vital inclusion criteria are evident as well as available clinical information and pre-operative radiographs. <br>(ii) The screening criteria: (a) radiographs were from patients diagnosed between 2013 and 2022 (b) in selected three hospitals; (c) radiographs with robust quality for reliable assessments of the bone lesions and (d) all of these radiographs were pre-operative. <br>Clinical characteristics like age, gender, and the location of the lesion of interest and so on were obtained from the patients' electronic medical records after data desensitization and standardization.<br>Radiographs were kept and downloaded as Digital Imaging and Communications in Medicine (DICOM) files from the picture archiving and communication system (PACS) at their original sizes and resolutions. All of these radiograph images have undergone desensitization processing of disengaging patient protected health information from DICOM data to meet the relevant legal criteria and requirements of US (HIPAA) as well as European (GDPR). Delineating the region of interest (ROI) was performed by two proficient radiologists. ROIs were meticulously outlined via Click 2 Crop (version 5.2.2) (https://click-2-crop.en.softonic.com/) to closely segment pertinent entities present in each PBT or bone infection. The smallest rectangular box that can completely cover the ROI was manually annotated as the boundary box by senior seniority radiologist to ensure accuracy. Afterwards, the annotated ROIs were used as ground truth for the model development process.</p>
ALS molecular subtypes are a combination of cellular and pathological features learned by deep multiomics classifiers (snRNA-seq)
GEO Series GSE271156. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Computer-Aided diagnostic for classifying Chest X-Ray Images Using Deep Ensemble Learning
<p>Both healthy and tuberculosis images are from "Tuberculosis (TB) Chest X-Ray Database'' collected by researchers from Qatar and Dhaka University, Doha, Qatar, and collaboration with doctors from Hamad Medical Corporation and Bangladesh. All the images are CRX in PNG format and a size 512x512. </p> <p>The COVID and Pneumonia images are also from a Kaggle dataset: "COVID-19 Radiography Database'' , which collects images from different sources. This dataset was collected by the same researchers as the "Tuberculosis (TB) Chest X-Ray Database''. All the images in this database are CRX in PNG format and a size 256X256.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.