Location History:
- Bayern, DE (2019)
- Munich, DE (2021 - 2023)
Company Filing History:
3 out of 6 
2 out of 51,920 
Years Active: 2019-2023
Title: Ansh Kapil: Innovator in Computational Pathology
Introduction
Ansh Kapil is a prominent inventor based in Munich, Germany. He has made significant contributions to the field of computational pathology, particularly in developing methods that enhance patient care through advanced technology. With a total of 5 patents, his work focuses on utilizing deep learning techniques to improve cancer treatment outcomes.
Latest Patents
One of Ansh Kapil's latest patents is a deep learning method for predicting patient response to therapy. This innovative method relies on spatial statistical analysis of classes of cell centers in digital images of tissue from cancer patients. The process begins with detecting cell centers in stained tissue images. For each detected cell center, an image patch is extracted, and a feature vector is generated using a convolutional neural network. Each cell center is then assigned a class based on its feature vector. A score is computed for the tissue image through spatial statistical analysis of the cell center classes. This score indicates how the cancer patient will respond to the predetermined therapy, which is recommended if the score exceeds a specific threshold.
Career Highlights
Ansh Kapil has worked with notable companies in the healthcare and technology sectors. He has been associated with AstraZeneca Computational Pathology GmbH and General Electric Company, where he has contributed to advancements in medical imaging and analysis. His expertise in deep learning and computational methods has positioned him as a key player in the field.
Collaborations
Throughout his career, Ansh has collaborated with talented professionals, including Nicolas Brieu and Sundeep R Patil. These collaborations have fostered innovation and have led to the development of cutting-edge technologies in computational pathology.
Conclusion
Ansh Kapil's work exemplifies the intersection of technology and healthcare, showcasing how innovations in deep learning can significantly impact patient treatment. His contributions continue to pave the way for advancements in cancer therapy and patient care.