Supplementary materials for manuscript: Self-absorption Correction in X-ray Fluorescence Computed Tomography with Deep Convolutional Neural Network, 

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Dataset including over 40,000 generated images of malicious binaries for malware classification in machine learning as outlined in NARAD - A Novel Auto-learn Real-time Fuzzy Machine Learning Anomaly Detection and Classification System.

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1167 Views

Medical Symptoms are sometimes very tricky to analyse in real time as it takes time for example, first to detect the symptoms then to perform some tests and finally coming to a solution. This process can be eliminated and lot of time can be saved by introducing the concept of Deep learning. CNNs create a network for extracting the features of a given image in order to evaluate the image based on the conditions required. This property of the CNN is used as a certain advantage in order to detect the symptoms based on the type of X-ray images provided.

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291 Views

The Objects Mosaic Hyperspectral Database contains 10,666 hyperspectral cubes of size 256x256x29 in the 420-700nm spectral range. This original hyperspectral database of real objects was experimentally acquired as described in the paper "SHS-GAN: Synthetic enhancement of a natural hyperspectral database", by J. Hauser, G. Shtendel, A. Zeligman, A. Averbuch, and M. Nathan, in the IEEE Transactions on Computational Imaging.

In addition, the database contains the SHS-GAN algorithm, which enables to generate synthetic database of hyperspectral images. 

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The dataset consists of 751 videos, each containing the performance one of the handball actions out of 7 categories (passing, shooting, jump-shot, dribbling, running, crossing, defence). The videos were manually extracted from longer videos recorded in handball practice sessions. 

Instructions: 

The directory scenes/ contains the videos in mp4 format with actions of interest performed in context of other players present in the scene. The files are arranged in subdirectories according to the action class of the action of interest. The directory actions/ contains the videos of performances of actions by single players isolated from the videos in scenes directory. The files are arranged in subdirectories according to the performed action class. Files are named so that the beginning of the name matches the original video from which the action is extracted. The directory player_detections/ contains the object detections for each frame in the videos.

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Of late, efforts are underway to build computer-assisted diagnostic tools for cancer diagnosis via image processing. Such computer-assisted tools require capturing of images, stain color normalization of images, segmentation of cells of interest, and classification to count malignant versus healthy cells. This dataset is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer. The images are provided after stain color normalization.

Instructions: 

IMPORTANT:

If you use this dataset, please cite below publications-

  1. Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images," Medical Image Analysis, vol. 65, Oct 2020. DOI: https://doi.org/10.1016/j.media.2020.101788. (2020 IF: 11.148)
  2. Shiv Gehlot, Anubha Gupta and Ritu Gupta, "EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 1389-1393.
  3. Anubha Gupta, Pramit Mallick, Ojaswa Sharma, Ritu Gupta, and Rahul Duggal, "PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma," PLoS ONE 13(12): e0207908, Dec 2018. DOI: 10.1371/journal.pone.0207908
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This dataset contains images obtained in image processing experiments presented in our paper "A Hybrid Radix–4 and Approximate Logarithmic Multiplier for Energy Efficient Image Processing", which is currently under review. 

Instructions: 

Dataset organization

  • Image_multiplication/ -> Image multiplication with grey level bits mask for approximate multipliers
  • Image_smoothing/ ->  Image smoothing with approximate multipliers
  • JPEG/ -> Lossy compression of images with approximate multipliers
  • Original/ -> Original images

 

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The dataset used in the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos" is provided here. It contains both the segmentation and classification images with manual labels. 

Instructions: 

The dataset contains the ultrasound mouse embyro images with manual labels. For more detail, please look into each subfolder and the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos". Or you can contact the author by zq415@nyu.edu if you have any question about the dataset and the paper. Thanks!

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186 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query.

 

Classes in this dataset:

Airplane

Baseball Diamond

Buildings

Freeway

Golf Course

Harbor

Intersection

Mobile home park

Overpass

Parking lot

River

Runway

Storage tank

Tennis court

Paper

The paper is also available on ArXiv: A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

 

Feel free to cite the author, if the work is any help to you:

 

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

 

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375 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

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84 Views

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