OBJECT DETECTION IN MEDICAL IMAGE

1 . A method to detect an object in a medical image, the method comprising:

receiving the medical image as an input;

partitioning the medical image into sub-regions;

detecting parts of an object in a selection of the sub-regions using a deep-learning neural network (DNN) model;

determining bounding boxes for the selection, wherein each of the bounding boxes are evaluated based on a confidence score detected as above a threshold level, and wherein the confidence score designates the parts as contained within the selection;

determining a region of interest (ROI) as a group comprising the selection, wherein similar orientations associated with the bounding boxes are comparable to similar orientations of a positive training model of the DNN model;

designating the selection as the ROI within the medical image; and

providing the medical image with the ROI to a user;

wherein the partitioning and detecting steps are performed before the determining steps, designating step, and providing step.

2 . The method of claim 1 , wherein the similar orientations associated with the bounding boxes include similar angular orientations between the parts of the object.

3 . The method of claim 1 , wherein the similar orientations include similar distances between the parts of the object.

4 . The method of claim 1 , further comprising:

applying a non-maximum suppression (NMS) mechanism to the selection to obtain a set of object bounding boxes.

5 . The method of claim 1 , wherein the DNN model associated with a detection of the parts of the object includes a region based convolutional neural network (R-CNN) model, a fast R-CNN model, a faster R-CNN model, a you only look once (YOLO) model, or a single shot multi-box (SSD) model.

6 . The method of claim 1 , wherein the sub-regions within the ROI are labelled with one or more annotations associated with a type of tissue.

7 . The method of claim 6 , wherein the type of tissue includes lobulated, spiculated, angular, clear boundary, oval, circumscribed, or abrupt interface.

8 . The method of claim 6 , wherein the one or more annotations include a relative position associated with a part of the object, and wherein the relative position includes top, bottom, left side, or right side.

9 . The method of claim 6 , further comprising:

providing a user interface associated with the ROI, wherein the user interface is configured to allow the user to change the one or more annotations associated with the ROI.

10 . The method of claim 9 , further comprising:

detecting the user providing a change to the one or more annotations to personalize the one or more annotations to the user; and

identifying a rate of concordance of the user in relation to the DNN model.

11 . The method of claim 10 , further comprising:

determining the concordance rate of the user as above a threshold;

re-training the DNN model based on the change to the one or more annotations;

re-processing the selection based on the change to the one or more annotations; and

re-labelling the sub-regions of the ROI based on the change to the one or more annotations to personalize the one or more annotations to one or more preferences of the user.

12 . The method of claim 10 , further comprising:

determining the concordance rate of the user as below a threshold;

rejecting the change to the one or more annotations; and

providing a notification to the user to re-evaluate the change to the one or more annotations.

13 . The method of claim 1 , wherein the ROI includes a lesion.

14 . The method of claim 1 , wherein a training mechanism associated with the DNN model includes a compensation for an unbalanced training data consisting of a majority of training medical images with no lesion and a minority of training medical images with a lesion, and wherein the training mechanism includes a down-sampling of the majority, an up-sampling of the minority, or a utilization of a cost sensitive mechanism, a gradient boost machine, or a hard negative mining mechanism.

15 . A device to detect an object in a medical image, wherein the device is configured to:

receive the medical image as an input;

partition the medical image into sub-regions;

detect parts of the object in a selection of the sub-regions using a deep-learning neural network (DNN) model;

determine bounding boxes for the selection, wherein each of the bounding boxes are evaluated based on a confidence score detected as above a threshold level, and wherein the confidence score designates the parts as contained within the selection;

determine a region of interest (ROI) as a group comprising the selection, wherein similar orientations associated with the bounding boxes are comparable to similar orientations of a positive training model of the DNN model;

designate the selection as the ROI within the medical image;

label the selection within the ROI with one or more annotations associated with a type of tissue, wherein the type of tissue includes lobulated, spiculated, angular, clear boundary, oval, circumscribed, or abrupt interface; and

provide the medical image with the one or more annotations and the ROI to a user;

wherein the partitioning and detecting steps are performed before the determining steps, designating step, labeling step, and providing step.

16 . A device for detecting an object in a medical image, the device comprising:

a memory configured to store instructions associated with an image analysis application,

a processor coupled to the memory, the processor executing the instructions associated with the image analysis application, wherein the image analysis application includes:

a computer assisted detection module configured to:

receive the medical image as an input;

partition the medical image into sub-regions;

detect parts of the object in a selection of the sub-regions using a deep-learning neural network (DNN) model;

determine bounding boxes for the selection, wherein each of the bounding boxes are evaluated based on a confidence score detected as above a threshold level, and wherein the confidence score designates the parts as contained within the selection;

determine a region of interest (ROI) as a group comprising the selection, wherein similar orientations associated with the bounding boxes are comparable to similar orientations of a positive training model of the DNN model;

designate the selection as the ROI within the medical image;

label the selection within the ROI with one or more annotations associated with a type of tissue, wherein the type of tissue includes lobulated, spiculated, angular, clear boundary, oval, circumscribed, or abrupt interface; and

provide the medical image with the one or more annotations and the ROI to a user;

wherein the partitioning and detecting steps are performed before the determining steps, designating step, labeling step, and providing step.