(A) Schematic of a convolutional neural network (CNN) with input (red circles marked with an “I”), multiple hidden layers of the network (orange circles representing layers n through n+x, where x is the number of layers and was 82 in the optimal CNN), and the output (green circles marked with an “O”). (B and C) Images of Burkitt lymphoma (B) and diffuse large B-cell lymphoma (C) with subcroppings used for input data for the training of the CNN indicated by the black box insets (reprinted with permission from the ASH Image Bank). (D) An example of an image of DLBCL that was diagnosed incorrectly as BL due to the large number of infiltrating small lymphocytes (reprinted with permission from Mohlman JS et al. Improving augmented human intelligence to distinguish Burkitt lymphoma from diffuse large B-cell lymphoma cases. Am J Clin Pathol, 2020; doi:10.1093/ajcp/aqaa001).

(A) Schematic of a convolutional neural network (CNN) with input (red circles marked with an “I”), multiple hidden layers of the network (orange circles representing layers n through n+x, where x is the number of layers and was 82 in the optimal CNN), and the output (green circles marked with an “O”). (B and C) Images of Burkitt lymphoma (B) and diffuse large B-cell lymphoma (C) with subcroppings used for input data for the training of the CNN indicated by the black box insets (reprinted with permission from the ASH Image Bank). (D) An example of an image of DLBCL that was diagnosed incorrectly as BL due to the large number of infiltrating small lymphocytes (reprinted with permission from Mohlman JS et al. Improving augmented human intelligence to distinguish Burkitt lymphoma from diffuse large B-cell lymphoma cases. Am J Clin Pathol, 2020; doi:10.1093/ajcp/aqaa001).

This Feature Is Available To Subscribers Only

or Create an Account

Close Modal
Close Modal