US 12,444,049 B2
Method and system for semi-supervised domain adaptation based universal lesion detection
Manu Sheoran, Noida (IN); Monika Sharma, Noida (IN); and Lovekesh Vig, Noida (IN)
Assigned to TATA CONSULTANCY SERVICES LIMITED, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Jul. 3, 2023, as Appl. No. 18/346,662.
Claims priority of application No. 202221041052 (IN), filed on Jul. 18, 2022.
Prior Publication US 2024/0020834 A1, Jan. 18, 2024
Int. Cl. G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06T 7/60 (2017.01); G06T 7/73 (2017.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC G06T 7/0012 (2013.01) [G06T 7/60 (2013.01); G06T 7/73 (2017.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10072 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A processor implemented method, the method comprising:
receiving, by one or more hardware processors, an input image, wherein the input image is obtained from a diagnostic medical imaging equipment;
extracting, by the one or more hardware processors, a plurality of multi-scale feature maps from the input image using a Feature Pyramid Network (FPN) based feature extraction framework;
generating, by the one or more hardware processors, a classification map based on the plurality of multi-scale feature maps using a Fully Connected Classifier Network (FCCN), wherein the FCCN classifies each of a plurality of pixels associated with each of the plurality of multi-scale feature maps into one of, a) a foreground pixel, and b) a background pixel based on a corresponding conditional probability;
computing, by the one or more hardware processors, a 4D vector corresponding to each of a plurality of foreground pixels using a bounding box regressor network, wherein the 4D vector encodes a location of a corresponding bounding box;
predicting, by the one or more hardware processors, an objectness score corresponding to each of the plurality of foreground pixels using a Fully Connected Prediction Network (FCPN), wherein the objectness score is a confidence score for being one of, the foreground pixel and the background pixel;
computing, by the one or more hardware processors, a centerness score for each of the plurality of foreground pixels using a single centerness network, wherein the centerness score represents a distance between the pixel and a center of a corresponding ground truth bounding box;
computing, by the one or more hardware processors, an updated objectness score for each of the plurality of foreground pixels by multiplying a corresponding centerness score with the corresponding predicted objectness score; and
detecting, by the one or more hardware processors, a plurality of multi-sized lesions in the input image based on the updated objectness score corresponding to each of the plurality of foreground pixels and corresponding 4D vector using a trained few-shot adversarial lesion detector network, wherein the few-shot adversarial lesion detector network is trained using a periodic gradient updation based overfitting aware few-shot learning mechanism.