Patents by Inventor Dmitry Goldgof
Dmitry Goldgof has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12229959Abstract: Systems and methods for automated stereology using deep learning are disclosed. The systems include an update in the form of a semi-automatic approach for ground truth preparation in 3D stacks of microscopy images (disector stacks) for generating more training data. The systems also present an exemplary disector-based MIMO framework where all the planes of a 3D disector stack are analyzed as opposed to a single focus-stacked image (EDF image) per stack. The MIMO approach avoids the costly computations of 3D deep learning-based methods by using the 3D context of cells in disector stacks; and prevents stereological bias in the previous EDF-based method due to counting profiles rather than cells and under-counting overlap-ping/occluded cells. Taken together, these improvements support the view that AI-based automatic deep learning methods can accelerate the efficiency of unbiased stereology cell counts without a loss of accuracy or precision as compared to conventional manual stereology.Type: GrantFiled: October 21, 2022Date of Patent: February 18, 2025Assignees: UNIVERSITY OF SOUTH FLORIDA, STEREOLOGY RESOURCE CENTER, INC.Inventors: Palak Pankajbhai Dave, Dmitry Goldgof, Lawrence O. Hall, Peter R. Mouton
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Patent number: 11992331Abstract: A Neonatal CNN (N-CNN) is provided for detecting neonatal pain emotion based upon facial recognition. A cascaded N-CNN is trained using a Neonatal Pain Assessment Database (NPAD) to automatically identify a neonatal patient experience pain in real-time. These results show that the automatic recognition of neonatal pain provided by the embodiments of the present invention is a viable and more efficient alternative to the current standard of pain assessment.Type: GrantFiled: October 19, 2020Date of Patent: May 28, 2024Assignee: University of South FloridaInventors: Ghadh Alzamzmi, Dmitry Goldgof, Rangachar Kasturi, Terri Ashmeade, Yu Sun, Rahul Paul, Md Sirajus Salekin
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Patent number: 11803968Abstract: Systems and methods for automated stereology are provided. A method can include providing an imager for capturing a Z-stack of images of a three-dimensional (3D) object; constructing extended depth of field (EDF) images from the Z-stack of images; performing a segmentation method on the EDF images including estimating a Gaussian Mixture Model (GMM), performing morphological operations, performing watershed segmentation, constructing Voronoi diagrams and performing boundary smoothing; and determining one or more stereology parameters such as number of cells in a region.Type: GrantFiled: May 5, 2021Date of Patent: October 31, 2023Assignees: UNIVERSITY OF SOUTH FLORIDA, STEREOLOGY RESOURCE CENTER, INC.Inventors: Peter Randolph Mouton, Hady Ahmady Phoulady, Dmitry Goldgof, Lawrence O. Hall
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Publication number: 20230309915Abstract: A computer-based system and method for generating a pain score of a subject using one or more sensory signals extracted from an AV signal of the subject. The AV signal may comprise one or more sensory signals including a face sensory signal, a body sensory signal and an audio sensory and wherein one or more of the sensory signals is missing from the AV signal.Type: ApplicationFiled: April 17, 2023Publication date: October 5, 2023Inventors: Md Sirajus Salekin, Ghadh Alzamzmi, Dmitry Goldgof, Yu Sun, Thao Ho, Peter Randolph Mouton
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Patent number: 11763450Abstract: The present disclosure describes a multi-initialization ensemble-based defense strategy against an adversarial attack. In one embodiment, an exemplary method includes training a plurality of conventional neural networks (CNNs) with a training set of images, wherein the images include original images and images modified by an adversarial attack; after training of the plurality of conventional neural networks, providing an input image to the plurality of conventional neural networks, wherein the input image has been modified by an adversarial attack; receiving a probability output for the input image from each of the plurality of conventional neural networks; producing an ensemble probability output for the input image by combining the probability outputs from each of the plurality of conventional neural networks; and labeling the input image as belonging to one of the one or more categories based on the ensemble probability output.Type: GrantFiled: November 16, 2020Date of Patent: September 19, 2023Assignees: UNIVERSITY OF SOUTH FLORIDA, H. LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE, INC.Inventors: Rahul Paul, Dmitry Goldgof, Lawrence Hall, Matthew Schabath, Robert Gillies
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Publication number: 20230274562Abstract: Bootstrapped semantic preprocessing techniques for medical datasets such as whole slide histopathology image datasets can be used to more efficiently and effectively train artificial intelligence used for medical purposes. The bootstrapped semantic preprocessing techniques generally include deriving metrics from image features and adjusting images according to the metrics. This process can be repeated iteratively for unknown and unlabeled data using a bootstrapping technique to normalize unknown samples to the training dataset distribution.Type: ApplicationFiled: February 27, 2023Publication date: August 31, 2023Inventors: Christopher Collazo, Lawrence Hall, Dmitry Goldgof, Samuel Wickline, Hua Pan
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Publication number: 20230127698Abstract: Systems and methods for automated stereology using deep learning are disclosed. The systems include an update in the form of a semi-automatic approach for ground truth preparation in 3D stacks of microscopy images (disector stacks) for generating more training data. The systems also present an exemplary disector-based MIMO framework where all the planes of a 3D disector stack are analyzed as opposed to a single focus-stacked image (EDF image) per stack. The MIMO approach avoids the costly computations of 3D deep learning-based methods by using the 3D context of cells in disector stacks; and prevents stereological bias in the previous EDF-based method due to counting profiles rather than cells and under-counting overlap-ping/occluded cells. Taken together, these improvements support the view that AI-based automatic deep learning methods can accelerate the efficiency of unbiased stereology cell counts without a loss of accuracy or precision as compared to conventional manual stereology.Type: ApplicationFiled: October 21, 2022Publication date: April 27, 2023Inventors: Palak Pankajbhai Dave, Dmitry Goldgof, Lawrence O. Hall, Peter R. Mouton
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Patent number: 11631280Abstract: A computer-based system and method for generating a current pain assessment of a neonate using facial expressions along with crying sounds, body movement, and vital signs changes and for using the current pain objective assessment to predict future pain objective assessment and assign a future pain probability score by incorporation spatiotemporal data into the multimodal assessment.Type: GrantFiled: November 9, 2020Date of Patent: April 18, 2023Assignee: University of South FloridaInventors: Peter Randolph Mouton, Sammie Lee Elkins, Md Sirajus Salekin, Dmitry Goldgof, Yu Sun, Thao Ho, Ghadh Alzamzmi
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Publication number: 20230022554Abstract: Methods and systems for imaging and analysis are described. Accurate pressure ulcer measurement is critical in assessing the effectiveness of treatment. However, the traditional measuring process is subjective. Each health care provider may measure the same wound differently, especially related to the depth of the wound. Even the same health care provider may obtain inconsistent measurements when measuring the same wound at different times. Also, the measuring process requires frequent contact with the wound, which increases risk of contamination or infection and can be uncomfortable for the patient. The present application describes a new automatic pressure ulcer monitoring system (PrUMS), which uses a tablet connected to a 3D scanner, to provide an objective, consistent, non-contact measurement method. The present disclosure combines color segmentation on 2D images and 3D surface gradients to automatically segment the wound region for advanced wound measurements.Type: ApplicationFiled: October 14, 2020Publication date: January 26, 2023Inventors: Matthew J. Peterson, Linda J. Cowan, Kimberly S. Hall, Dmitry Goldgof, Sudeep Sarkar, Chih-Yun Pai, Hunter Morera, Yu Sun
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Publication number: 20220058369Abstract: Systems and methods for automated stereology are provided. In some embodiments, an active deep learning approach may be utilized to allow for a faster and more efficient training of a deep learning model for stereology analysis. In other embodiments, existing deep learning models for stereology analysis may be re-tuned to develop greater accuracy for a given data set of interest, either with or without an active deep learning approach.Type: ApplicationFiled: August 9, 2021Publication date: February 24, 2022Inventors: Saeed S. Alahmari, Dmitry Goldgof, Lawrence O. Hall, Peter R. Mouton
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Patent number: 11202604Abstract: A system and method of automatically assessing pediatric and neonatal pain using facial expressions along with crying sounds, body movement, and vital signs change to improve the diagnosis and treatment of pain in the pediatric patient population.Type: GrantFiled: April 19, 2019Date of Patent: December 21, 2021Assignee: University of South FloridaInventors: Ghadh Alzamzmi, Chih-Yun Pai, Dmitry Goldgof, Rangachar Kasturi, Terri Ashmeade, Yu Sun
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Patent number: 11004199Abstract: Systems and methods for automated stereology are provided. A method can include providing an imager for capturing a Z-stack of images of a three-dimensional (3D) object; constructing extended depth of field (EDF) images from the Z-stack of images; performing a segmentation method on the EDF images including estimating a Gaussian Mixture Model (GMM), performing morphological operations, performing watershed segmentation, constructing Voronoi diagrams and performing boundary smoothing; and determining one or more stereology parameters such as number of cells in a region.Type: GrantFiled: November 10, 2017Date of Patent: May 11, 2021Assignees: UNIVERSITY OF SOUTH FLORIDA, STEREOLOGY RESOURCE CENTER, INC.Inventors: Peter Randolph Mouton, Hady Ahmady Phoulady, Dmitry Goldgof, Lawrence O. Hall
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Publication number: 20210052215Abstract: A computer-based system and method for generating a current pain assessment of a neonate using facial expressions along with crying sounds, body movement, and vital signs changes and for using the current pain objective assessment to predict future pain objective assessment and assign a future pain probability score by incorporation spatiotemporal data into the multimodal assessment.Type: ApplicationFiled: November 9, 2020Publication date: February 25, 2021Inventors: Peter Randolph Mouton, Sammie Lee Elkins, Md Sirajus Salekin, Dmitry Goldgof, Yu Sun, Thao Ho, Ghadh Alzamzmi
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Publication number: 20210030354Abstract: A Neonatal CNN (N-CNN) is provided for detecting neonatal pain emotion based upon facial recognition. A cascaded N-CNN is trained using a Neonatal Pain Assessment Database (NPAD) to automatically identify a neonatal patient experience pain in real-time. These results show that the automatic recognition of neonatal pain provided by the embodiments of the present invention is a viable and more efficient alternative to the current standard of pain assessment.Type: ApplicationFiled: October 19, 2020Publication date: February 4, 2021Inventors: Ghadh Alzamzmi, Dmitry Goldgof, Rangachar Kasturi, Terri Ashmeade, Yu Sun, Rahul Paul, Md Sirajus Salekin
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Patent number: 10827973Abstract: A system and method for measuring an infant's pain intensity is presented. The method for assessing an infant's pain intensity based on facial expressions is comprised of three main stages: detection of an infant's face in video sequence followed by preprocessing operations including face alignment; expression segmentation; and expression recognition or classification. Also presented is a multimodal system for assessing an infant's pain intensity using the following classifiers: facial expression classifier; vital sign classifier; crying recognition classifier; body motion classifier and state of arousal classifier. Each classifier generates an individual score, all of which are normalized and weighed to generate a total pain score that indicates pain intensity.Type: GrantFiled: January 6, 2016Date of Patent: November 10, 2020Assignee: University of South FloridaInventors: Ghadh A. Alzamzmi, Dmitry Goldgof, Yu Sun, Rangachar Kasturi, Terri Ashmeade
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Patent number: 10713787Abstract: Systems and methods for applying an ensemble of segmentations to microscopy images of a tissue sample to determine if the tissue sample is representative of cancerous tissue. The ensemble of segmentations is applied to a plurality of greyscale or color microscopy images to generate a final image level segmentation and a final blob level segmentation. The final image level segmentation and final blob level segmentation are used to calculate a mean nuclear volume to determine if the tissue sample is representative of cancerous tissue.Type: GrantFiled: October 1, 2018Date of Patent: July 14, 2020Assignees: University of South Florida, Stereology Resource Center, Inc.Inventors: Peter Randolph Mouton, Dmitry Goldgof, Lawrence O. Hall, Baishali Chaudhury
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Publication number: 20190320974Abstract: A system and method of automatically assessing pediatric and neonatal pain using facial expressions along with crying sounds, body movement, and vital signs change to improve the diagnosis and treatment of pain in the pediatric patient population.Type: ApplicationFiled: April 19, 2019Publication date: October 24, 2019Applicant: University of South FloridaInventors: Ghadh Alzamzmi, Chih-Yun Pai, Dmitry Goldgof, Rangachar Kasturi, Terri Ashmeade, Yu Sun
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Patent number: 10298391Abstract: A system for generating symmetric cryptographic keys for communications between hosts. Hosts use associated devices to generate secret keys. Each key is generated based on a static seed and a dynamic seed. The dynamic seed is created from sensor data or auxiliary data. The secret key allows host machines to encrypt, or decrypt, plaintext messages sent to, or received from, other host machines.Type: GrantFiled: September 19, 2018Date of Patent: May 21, 2019Assignee: University of South FloridaInventors: Jay Ligatti, Cagri Cetin, Shamaria Engram, Dmitry Goldgof
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Patent number: 10291403Abstract: A system for generating symmetric cryptographic keys for communications between hosts. Hosts use associated devices to generate secret keys. Each key is generated based on a static seed and a dynamic seed. The dynamic seed is created from sensor data or auxiliary data. The secret key allows host machines to encrypt, or decrypt, plaintext messages sent to, or received from, other host machines.Type: GrantFiled: July 9, 2018Date of Patent: May 14, 2019Assignee: University of South FloridaInventors: Jay Ligatti, Cagri Cetin, Shamaria Engram, Dmitry Goldgof
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Publication number: 20190043189Abstract: Systems and methods for applying an ensemble of segmentations to microscopy images of a tissue sample to determine if the tissue sample is representative of cancerous tissue. The ensemble of segmentations is applied to a plurality of greyscale or color microscopy images to generate a final image level segmentation and a final blob level segmentation. The final image level segmentation and final blob level segmentation are used to calculate a mean nuclear volume to determine if the tissue sample is representative of cancerous tissue.Type: ApplicationFiled: October 1, 2018Publication date: February 7, 2019Inventors: Peter Randolph Mouton, Dmitry Goldgof, Lawrence O. Hall, Baishali Chaudhury