File:Fig1 Signoroni NatComm23 14.png

Figure 1. a The full laboratory automation (FLA) workflow and the role of the proposed solutions. b DeepColony hierarchical architecture of the operational and decision-making processes. c Example of “enumeration map” (level 0 output) where the cardinality of colony aggregates is indicated with colors and numbers. An “X” appears on colonies discarded because they are located in color distortion areas. d Example (on the same plate) of selected “good colonies” (level 1 output) with their presumptive ID (level 2 output). An “X” appears on colonies discarded because they are not single or too close to the plate border or to confluence areas. Detected species S_i are indicated for each colony with colors and corresponding legend. e Outputs of level 3 and 4 are shown on the same plate. G_i refers to species ID updated after similarity-driven contextual grouping, while subsequent clinical relevance interpretation indicates a significant mixed flora plate, where best colonies eligible for picking are indicated with arrows. f Convolutional neural network (CNN) architecture for single colony presumptive ID (red box at level 2). g “Siamese CNN” scheme (red box at level 3) for contextual ID.

Signoroni, Alberto; Ferrari, Alessandro; Lombardi, Stefano; Savardi, Mattia; Fontana, Stefania; Culbreath, Karissa (2023). "Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology". Nature Communications 14: 6874. doi:10.1038/s41467-023-42563-1. 

2023

Signoroni, Alberto; Ferrari, Alessandro; Lombardi, Stefano; Savardi, Mattia; Fontana, Stefania; Culbreath, Karissa

Creative Commons Attribution 4.0 International