Tags: deep learning

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  1. Raw Data and Results from Automatic Murine Cardiac Ultrasound and Photoacoustic Image Segmentations

    2023-05-25 15:33:38 | Datasets | Contributor(s): Katherine Leyba, Hayley Chan, Olivia Claire Loesch, Pierre Sicard, Craig J Goergen | doi:10.4231/9C8X-H052

    Accuracy and dice scores from cross-validation reported in the cross-validation results spreadsheet. Radial strain raw data and results reported in strain results spreadsheet. Oxygen saturation values reported in sO2 results spreadsheet.

    https://purr.purdue.edu/publications/4287

  2. Aerial Fluvial Image Dataset (AFID) for Semantic Segmentation

    2022-07-20 19:06:01 | Datasets | Contributor(s): Zihan Wang, Li-Fan Wu, Nina Mahmoudian | doi:10.4231/B129-XD47

    816 2K/2.7K per-pixel annotated images with 8 classes: River, Boat, Bridge, Sky, Forest vegetation, Dry sediment, Drone self and Obstacle in river. Fluvial scenes are from Wabash River and Wildcat Creek in Indiana, USA.

    https://purr.purdue.edu/publications/4105

  3. River Obstacle Segmentation En-route By USV Dataset (ROSEBUD)

    2022-06-12 14:00:00 | Datasets | Contributor(s): Reeve David Lambert, Jianwen Li, Jalil Francisco Chavez Galaviz, Zihan Wang, Nina Mahmoudian | doi:10.4231/MMJ2-NH88

    This dataset contains stills from video taken on Sugar Creek and the Wabash River in the US state of Indiana. Images are hand annotated to provide training and testing data for semantic segmentation networks.

    https://purr.purdue.edu/publications/4072

  4. Wheat spike blast image classification using deep convolutional neural networks

    2021-04-29 20:01:56 | Datasets | Contributor(s): Mariela Fernandez-Campos, Yuting Huang, Mohammad Jahanshahi, Tao Wang, Jian Jin, Darcy Telenko, Carlos Gongora, Christian D Cruz | doi:10.4231/P0Y7-3428

    The folder includes i) a wheat spike blast image classification CNN model trained to automatically quantify and classify disease severity, ii) the generated datasets that include images of wheat spike blast severity levels under controlled...

    https://purr.purdue.edu/publications/3772

  5. Raman spectra-based deep learning – A tool to identify microbial contamination

    2020-09-03 01:18:11 | Datasets | Contributor(s): Murali K. Maruthamuthu, Amir Hossein Raffiee, Denilson Mendes De Oliveira, Arezoo M. Ardekani, Mohit S. Verma | doi:10.4231/HQGB-K827

    Raman spectroscopic dataset for the potential microbes contaminating the pharmaceutical industry.

    https://purr.purdue.edu/publications/3595

  6. Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

    2017-11-17 19:16:53 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7V98675

    This document includes the main source code (Matlab or Python) related to our study.

    https://purr.purdue.edu/publications/2816

  7. Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

    2017-09-24 21:22:47 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7V98675

    This document includes the main source code (Matlab or Python) related to our study.

    https://purr.purdue.edu/publications/2816

  8. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 2

    2017-09-18 19:45:42 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7NS0S1F

    This is a video-fMRI dataset for subject 2 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2806

  9. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests

    2017-09-15 15:14:52 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7SF2TCW

    This is a video-fMRI dataset contains the videos with stimuli acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2809

  10. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 3

    2017-09-15 14:43:12 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7J101BV

    This is a video-fMRI dataset for subject 3 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2807

  11. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 1

    2017-09-15 13:28:40 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7X63K3M

    This is a video-fMRI dataset for subject 1 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI),

    https://purr.purdue.edu/publications/2805

  12. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Stimuli

    2017-09-15 13:24:22 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R71Z42KK

    This is a video-fMRI dataset contains the videos with stimuli acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2808

  13. Visualized layer-wise visual features in deep residual neural network

    2017-06-29 12:24:17 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Wei Chen, Zhongming Liu | doi:10.4231/R7PR7T1G

    Deep residual neural network is a brain-inspired computational model. 50 layers of neuron-like computational units are stacked into a bottom-up hierarchy. Features encoded at units are visualized for intuitively understanding the internal...

    https://purr.purdue.edu/publications/2590

  14. Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

    2015-09-30 20:07:55 | Datasets | Contributor(s): Murat Dundar, Qiang Kou, Baichuan Zhang, Yicheng He, Bartlomiej P. Rajwa | doi:10.4231/R7N58J9Z

    A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity

    https://purr.purdue.edu/publications/1988

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