Systems and user interfaces for enhancement of data utilized in machine-learning based medical image review

1. A method for facilitating proper classification of features in medical images comprising:

generating, for display on a display of a computing system, an interactive classification user interface concurrently displaying a first group of medical images and a second group of medical images, wherein each of the first group of medical images depicts an object associated with a first classification and each of the second group of medical images depicts an object associated with a second classification;

detecting user input indicating movement of one or more medical images from the first group to the second group;

in response to the user input, re-classifying the one or more medical images as associated with the second classification; and

providing the re-classifications to a machine learning system.

2. The method of claim 1 , wherein the first group of medical images is displayed as a grid.

3. The method of claim 1 , wherein the first group of medical images is displayed as a stack.

4. The method of claim 1 , wherein each medical image is associated with a user who classified the medical image, and wherein the method further comprises:

accessing information indicating users associated with the moved medical images; and

generating respective notifications for presentation to the indicated users identifying the re-classification.

5. The method of claim 1 , wherein, the machine learning system updates based on analysis of object characteristics of the re-classified one or more medical images to increase an accuracy associated with automated assignment of classifications.

6. A method for facilitating proper classification of features in medical images comprising:

generating, for display on a display of a computing system, an interactive classification user interface concurrently displaying two groups of medical images, each group having been assigned a disparate classification relating to objects included in the medical images, and each object having been extracted from a larger medical image;

the interactive classification user interface enabling a user to adjust a classification of each medical image to correspond to a different group, such that the medical image is re-classified according to the different classification; and

providing the re-classifications to a machine learning system, the machine learning system updating based on the re-classifications to increase an accuracy associated with assigning classifications.

7. The method of claim 6 , wherein borders of the objects included in the medical images are highlighted, the highlighting being automatically generated by the machine learning system.

8. The method of claim 6 , wherein the interactive classification user interface displays each group of medical images as a respective grid.

9. The method of claim 6 , wherein the interactive classification user interface displays each group of medical images as a respective stack.

10. The method of claim 6 , wherein the interactive classification user interface receives input from the user on a touch-screen display, the input representing the user dragging a first image included in a first group to a second group, such that the first image is re-classified.

11. The method of claim 6 , wherein the interactive classification user interface is configured to receive input via a touch-sensitive display, and wherein received input directed to a particular medical image causes the interactive classification user interface to update to present a full image context related to the particular medical image.

12. The method of claim 6 , wherein a re-classification assigned by the user is stored, and wherein an initial user who classified the associated medical image is alerted.

13. The method of claim 6 , wherein a threshold number of users assign classifications to the medical images, and wherein a final classification is determined for each medical image prior to providing the classifications to the machine learning system.

14. The method of claim 6 , wherein the classifications relate to a diagnosis associated with an object, a shape of an object, or a change in size or character of an object over time.

15. Non-transitory computer storage-media storing instructions that when executed by a system of one or more computers, cause the one or more computers to perform operations comprising:

providing a visual concurrent display of two groups of medical images, each group being associated with a respective classification, wherein the classification relates to objects included in the medical images;

providing a user interface enabling a reviewing user to re-classify medical images, such that one or more medical images are assigned to a different group;

responsive to the reviewing user utilizing the user interface to indicate that a particular medical image is to be re-classified, updating the particular image to be assigned to the different group and causing storage of the update.

16. The computer storage-media of claim 15 , wherein the operations further comprise:

training a machine learning system based on the re-classification.

17. The computer storage-media of claim 15 , wherein each group is presented as a respective grid of medical images.

18. The computer storage-media of claim 15 , wherein each group is presented as a respective stack of medical images.

19. The computer storage-media of claim 15 , wherein an initial reviewing user who classified the particular medical image is alerted.

20. The computer storage-media of claim 15 , wherein a notification is provided to a third reviewing user, the notification enabling activation of an application on a user device of the third reviewing user to determine a final classification for the particular medical image.