Ehsan Dehghan Marvast

USPTO Granted Patents = 20 (Patent Applications = 1)

Average Co-Inventor Count = 3.6

ph-index = 2

Forward Citations = 16(Granted Patents)

Company Filing History:

16 out of 164,108

International Business Machines Corporation

where one patent can have more than one assignee

Years Active: 2019-2024

where 'Filed Patents' based on already Granted Patents

20 patents (USPTO):Sort by Forward Citations

Abstract keywords

1. 11967067 - Disease detection with masked attention (2024-04-23),

2. 11875898 - Automatic condition diagnosis using an attention-guided framework (2024-01-16),

3. 11830187 - Automatic condition diagnosis using a segmentation-guided framework (2023-11-28),

4. 11813113 - Automated extraction of echocardiograph measurements from medical images (2023-11-14),

5. 11694297 - Determining appropriate medical image processing pipeline based on machine learning (2023-07-04),

6. 11417424 - Disease detection from weakly annotated volumetric medical images using convolutional long short-term memory and multiple instance learning (2022-08-16),

7. 11195273 - Disease detection from weakly annotated volumetric medical images using convolutional long short-term memory (2021-12-07),

8. 11094034 - Determining appropriate medical image processing pipeline based on machine learning (2021-08-17),

9. 11080326 - Intelligently organizing displays of medical imaging content for rapid browsing and report creation (2021-08-03),

10. 10987013 - Automated extraction of echocardiograph measurements from medical images (2021-04-27),

11. 10930386 - Automated normality scoring of echocardiograms (2021-02-23),

12. 10910098 - Automatic summarization of medical imaging studies (2021-02-02),

13. 10740866 - Determining appropriate medical image processing pipeline based on machine learning (2020-08-11),

14. 10733265 - Medical image manager with automated synthetic image generator (2020-08-04),

15. 10719580 - Medical image manager with automated synthetic image generator (2020-07-21),

Ehsan Dehghan Marvast: Pioneering Innovations in Medical Imaging

Introduction

Ehsan Dehghan Marvast, based in Palo Alto, CA, is a prominent inventor known for his significant contributions to the field of medical imaging and condition detection. With an impressive portfolio of 20 patents, his work focuses on the intersection of deep learning and healthcare technologies.

Latest Patents

Among his latest innovations is the patent for "Disease detection with masked attention." This invention involves a candidate generator that produces a set of candidate three-dimensional image patches from input volumes. A candidate classifier then examines these patches to determine their potential for containing disease. This process employs attention masks that help to enhance essential image regions while minimizing confusion from non-relevant areas. Additionally, Ehsan has patented "Automatic condition diagnosis using an attention-guided framework," which involves methods for training computer-aided detection systems. This method utilizes multiple images from patients, applying deep learning networks to generate attention maps and probabilities for conditions, significantly improving diagnostic accuracy.

Career Highlights

Throughout his career, Ehsan has worked with reputed organizations such as IBM and Guerbet, where he applied his skills in developing cutting-edge technologies. His contributions not only enhance the effectiveness of medical imaging but also pave the way for more precise and automated diagnostic processes.

Collaborations

Ehsan has collaborated with notable individuals in his field, such as Tanveer Fathima Syeda-Mahmood and David James Beymer. These collaborative efforts have further strengthened his innovations in medical imaging and patent applications, leading to impactful results within the healthcare sector.

Conclusion

Ehsan Dehghan Marvast's work exemplifies the powerful fusion of technology and healthcare. Through his continuous innovation and numerous patents, he is influencing the future of disease detection and diagnosis, making significant strides that can lead to improved patient outcomes.

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