The morphology of leukemic lymphoblasts, as seen in the initial routine bone marrow aspirate, of 40 children with ALL has been evaluated using a computer-assisted automated microscope. A statistic, derived from a combination of the number of macrolymphoblasts and the percentage of cells with no cytoplasm was found to predict response to standard therapy and to identify patients with a high probability of long-term remission as well as a group with a relatively poor response to therapy.
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A framework for assessing reliability of observer annotations of aerial wildlife imagery, with insights for deep learning applications.
PLoS One
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Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America.
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View Article and Find Full Text PDFA high-throughput ResNet CNN approach for automated grapevine leaf hair quantification.
Sci Rep
January 2025
Julius Kühn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833, Siebeldingen, Germany.
The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling forces, thus preventing pathogen attack such as downy mildew and anthracnose. Moreover, leaf hairs as a favorable habitat may considerably affect the abundance of biological control agents.
View Article and Find Full Text PDFDeep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.
Sci Rep
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Department of Orthopaedic Surgery and Traumatology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
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View Article and Find Full Text PDFA super-resolution algorithm to fuse orthogonal CT volumes using OrthoFusion.
Sci Rep
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Divisions of Physical Therapy and Rehabilitation Science, Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, MN, 55455, USA.
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Nat Commun
January 2025
Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
Advancements in high-throughput screenings enable the exploration of rich phenotypic readouts through high-content microscopy, expediting the development of phenotype-based drug discovery. However, analyzing large and complex high-content imaging screenings remains challenging due to incomplete sampling of perturbations and the presence of technical variations between experiments. To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions.
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