Extended Data Fig. 3: Analysis of algorithm performance on the synthetic dataset (SHREC’20 challenge).
From: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms
a, Performance (F1-score) of DeepFinder, UMC and template matching algorithms and ability of algorithms to discriminate between 12 classes/subclasses of macromolecules. The highest (best) possible value of an F1-score is 1.0 and the lowest (worst) possible value is 0. The scores of template matching were provided by the SHREC’20 challenge organizers (Utrecht University, Department of Information and Computing Sciences and Department of Chemistry). b, Performance of DeepFinder implemented as a multi-class network architecture and as an architecture made of 12 binary networks. These two architectures differ only by the number of output neurons. c, Influence of the training target generation method (‘shapes’ versus ‘spheres’). In the case of ‘shapes’, the exact shapes of the macromolecules have been used to annotate the tomograms. In the case of ‘spheres’, the shape and the orientation of macromolecules are not needed to generate the training targets. This analysis used eight tomograms for training, one tomogram for validation, and one tomogram for testing.
