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9 results for “Domain generality”
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation for Domain Generalization Task
<p><strong>Description</strong></p> <p>This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task (MIMII DG). The dataset consists of normal and abnormal operating sounds of five different types of industrial machines, i.e., fans, gearboxes, bearing, slide rails, and valves. The data for each machine type includes three subsets called "sections", and each section roughly corresponds to a type of domain shift. <strong>This dataset is a subset of the dataset for <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2022 Challenge Task 2</a>, so the dataset is entirely the same as data included in the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a>. </strong>For more information, please see the pages of the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a> and the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">task description</a><strong> </strong>for DCASE 2022 Challenge Task 2.</p> <p> </p> <p><strong>Baseline system</strong></p> <p>Two simple baseline systems are available on the Github repositories <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_ae">autoencoder-based baseline</a> and <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_mobile_net_v2">MobileNetV2-based baseline</a>. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was made by <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>We will publish a paper on the dataset and will announce the citation information for them, so please make sure to cite them if you use this dataset.</p> <p> </p> <p><strong>Feedback</strong></p> <p>If there is any problem, pease contact us</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
MItosis DOmain Generalization Challenge 2022 (MICCAI MIDOG 2022), Training data set (PNG version)
<p>This is the training dataset of the MItosis DOmain Generalization (MIDOG) challenge 2022, held in conjunction with MICCAI 2022. Please find the structured challenge description at 10.5281/zenodo.6362337.</p> <p>The training set consists of 405 tumor cases in total across six tumor types:</p> <ul> <li>Canine Lung Cancer (44 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human Breast Cancer (150 cases, scanned using three scanners, part of MIDOG2021 dataset)</li> <li>Canine Lymphoma (55 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human neuroendocrine tumor (55 cases, scanned with Hamamatsu NanoZoomer XR)</li> <li>Canine Cutaneous Mast Cell Tumor (50 cases, scanned with Aperio ScanScope CS2)</li> <li>Human melanoma (51 cases, scanned with Hamamatsu NanoZoomer XR) (no labels provided)</li> </ul> <p>From each WSI, a trained pathologist selected an area of 2mm² corresponding to approximately 10 high power fields, according to the grading scheme of Elston and Ellis. We cropped this area and provide it as PNG files in this data set due to restrictions in data set size on zenodo. Each file includes the resolution (in dots per inch, DPI) of the original scanned images.</p> <p>The training set contains 9501 mitotic figures (MF) and 11051 hard examples (non-mitotic figures). All annotations are provided in MS COCO JSON format and as SQLITE database (SlideRunner format).</p>
Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms
<p>The question of whether perceptual expertise is mediated by general-expert or domain-specific processing mechanisms has been debated for decades. Because humans are experts in face recognition, face-like neural and cognitive effects for objects of expertise were considered to support for the general-expertise hypothesis. Conversely, stronger effects for faces than objects of expertise were considered to support the domain-specific hypothesis. However, the effects of domain, experience, and level of categorization, are confounded in human studies, which may lead to erroneous inferences. To overcome these limitations, we used computational models of perceptual expertise and tested different domains (objects, faces, birds) and levels of categorization (basic, sub-ordinate, individual) in isolation, matched for amount of experience. Like humans, the models generated a larger inversion effect for faces than for objects. Importantly, a face-like inversion effect was found for individual-based categorization of non-faces (birds) but only in a network specialized for that domain. Thus, contrary to prevalent assumptions, face-like effects in objects of expertise may originate from domain-specific rather than domain-general processing mechanisms. More generally, we show how deep learning algorithms can be used to isolate the effects of factors that are inherently confounded in the natural environment of biological organisms.</p>
Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms
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Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex (data & code)
<p><strong>Data and code for:</strong></p> <p>Apšvalka, D., Ferreira, C. S., Schmitz, T. W., Rowe, J. B., & Anderson, M. C. (2022). Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex. <em>Nature Communications, </em> <strong>13, </strong>274<em>.</em> <a href="https://doi.org/10.1038/s41467-021-27926-w"> https://doi.org/10.1038/s41467-021-27926-w</a></p> <blockquote> <p>Over the last two decades, inhibitory control has featured prominently in accounts of how humans and other organisms regulate their behaviour and thought. Previous work on how the brain stops actions and thoughts, however, has emphasised distinct prefrontal regions supporting these functions, suggesting domain-specific mechanisms. Here we show that stopping actions and thoughts recruits common regions in the right dorsolateral and ventrolateral prefrontal cortex to suppress diverse content, via dynamic targeting. Within each region, classifiers trained to distinguish action-stopping from action-execution also identify when people are suppressing their thoughts (and vice versa). Effective connectivity analysis reveals that both prefrontal regions contribute to action and thought stopping by targeting the motor cortex or the hippocampus, depending on the goal, to suppress their task-specific activity. These findings support the existence of a domain-general system that underlies inhibitory control and establish Dynamic Targeting as a mechanism enabling this ability.</p> </blockquote>
DAGHAR: A Benchmark for Domain Adaptation and Generalization in Smartphone-Based Human Activity Recognition
<p>DAGHAR benchmark is a curated dataset collection designed for domain adaptation and domain generalization studies in HAR tasks, using inertial sensors such as accelerometers and gyroscopes, from "A benchmark for domain adaptation and generalization in smartphone-based human activity recognition" work. It features raw inertial sensor data sourced exclusively from smartphones. Six public datasets were selected and standardized in terms of accelerometer units of measurement, sampling rate, gravity component, activity labels, user partitioning, and time window size. This standardization process allows for creating a comprehensive benchmark for evaluating the generalization capabilities of HAR models in cross-dataset scenarios.</p> <p>The benchmark is based on the following datasets:</p> <ul> <li><strong>Ku-HAR</strong>, from "Sikder, N. and Nahid, A.A., 2021. KU-HAR: An open dataset for heterogeneous human activity recognition. Pattern Recognition Letters, 146, pp.46-54", avaliable at <a href="https://data.mendeley.com/datasets/45f952y38r/5">Mendeley</a>. Distributed under CC BY 4.0.</li> <li><strong>MotionSense</strong>, from "Malekzadeh, M., Clegg, R.G., Cavallaro, A. and Haddadi, H., 2019, April. Mobile sensor data anonymization. In Proceedings of the international conference on internet of things design and implementation (pp. 49-58)", available at <a href="https://www.kaggle.com/datasets/malekzadeh/motionsense-dataset" target="_blank" rel="noopener">Kaggle</a>. Distributed under Open Data Commons Open Database License (ODbL) v1.0.</li> <li><strong>RealWorld</strong>, from "Sztyler, T. and Stuckenschmidt, H., 2016, March. On-body localization of wearable devices: An investigation of position-aware activity recognition. In 2016 IEEE international conference on pervasive computing and communications (PerCom) (pp. 1-9). IEEE", available at <a href="https://www.uni-mannheim.de/dws/research/projects/activity-recognition/dataset/dataset-realworld/" target="_blank" rel="noopener">this link</a>. We obtained explicitly permission to distribute a copy of the preprocessed data from the original authors.</li> <li><strong>UCI-HAR</strong>, from "Reyes-Ortiz, J.L., Oneto, L., Samà, A., Parra, X. and Anguita, D., 2016. Transition-aware human activity recognition using smartphones. Neurocomputing, 171, pp.754-767", available at <a href="https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones">UCI Repository</a>. Distributed under CC BY 4.0.</li> <li><strong>WISDM</strong>, from "Weiss, G.M., Yoneda, K. and Hayajneh, T., 2019. Smartphone and smartwatch-based biometrics using activities of daily living. Ieee Access, 7, pp.133190-133202", available at <a href="https://archive.ics.uci.edu/dataset/507/wisdm+smartphone+and+smartwatch+activity+and+biometrics+dataset">UCI repository</a>. Distributed under CC BY 4.0.</li> </ul>
A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup
<p>We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4].</p> <table> <caption>Details of this dataset</caption> <tbody> <tr> <td>Domain</td> <td>Cases in Each Domain<br> (Training/Test)</td> </tr> <tr> <td>REFUGE</td> <td>320/80</td> </tr> <tr> <td>Drishti-GS</td> <td>50/51</td> </tr> <tr> <td>ORIGA</td> <td>500/150</td> </tr> <tr> <td>BinRushed (RIGA)</td> <td>156/39</td> </tr> <tr> <td>Magrabia (RIGA)</td> <td>76/19</td> </tr> </tbody> </table> <p>[1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570.</p> <p>[2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56.</p> <p>[3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068.</p> <p>[4] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. SPIE, 2018, 10579: 55-62.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre> <p> </p>
Generalized and Domain-Specific Episodic Thinking for Smoking Cessation
ClinicalTrials.gov study NCT07158749. IPD Sharing: YES. Countries: 1. Publications: 0.
Domain-general and Domain-specific Contributions to the Development of Numerical Cognition
ClinicalTrials.gov study NCT06658392. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.