Application of deep learning for medical imaging evaluation
AssigneeQure.ai Technologies Private Limited
InventorsSasank Chilamkurhy, Rohit Ghosh, Swetha Tanamala, Pooja Rao, Prashant Warier
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Abstract
This disclosure generally pertains to methods and systems for processing electronic data obtained from imaging or other diagnostic and evaluative medical procedures. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems configured to detect and localize medical abnormalities on medical imaging scans by a deep learning algorithm.
Claims16
A method for developing a deep learning system to detect and localize medical abnormalities on non-contrast head CT scans, comprising:
- Selecting medical imaging scans and extracting medical abnormalities using natural language processing (NLP) algorithms, wherein each type of the medical abnormalities is annotated at scan, slice and pixel levels;
- Training a slice-wise deep learning algorithm with the selected medical imaging scans to segment pixel-level annotated scans;
- Training the deep learning algorithm with the selected medical imaging scans to generate slice-level confidences;
- Predicting confidences for a presence of each type of medical abnormalities;
- and
- Generating a score that corresponds to a level of recognition of the medical abnormalities and outputting a mask representing the precise location and extent of the medical abnormalities.
A system configured to detect and localize medical abnormalities on head CT scans using a deep learning algorithm carried out by a computer, wherein the deep learning algorithm is developed by the steps of:
- Selecting medical imaging scans and extracting medical abnormalities using natural language processing (NLP) algorithms, wherein each type of the medical abnormalities is annotated at scan, slice and pixel levels;
- Training a slice-wise deep learning algorithm with the selected medical imaging scans to segment pixel-level annotated scans;
- Training the deep learning algorithm with the selected medical imaging scans to generate slice-level confidences;
- Predicting confidences for a presence of each type of medical abnormalities;
- and
- Generating a score that corresponds to a level of recognition of the medical abnormalities and outputting a mask representing the precise location and extent of the medical abnormalities.
Dependencies are inferred from the claim text.