Day1: August 22, 2019
- Diagnostic Pathology
- Advance Technology in Digital Pathology
Location: Zurich, Switzerland
Dr Sekwaila Antoinette Masenya has completed her MBCHB from the University of Kwa-Zulu Natal. She is a registrar in the department of Anatomical pathology at National Health Laboratory services, Groote Schuur hospital, University of Cape Town.
Embyronal tumours of the central nervous system occur predominantly in young children but also affect adolescents and adults. Recently, four new molecular entities have been described, designated “CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)”, “CNS Ewing sarcoma family tumour with CIC alteration (CNS EFT-CIC)”, CNS high grade neuroepithelial tumour with MN1 alteration (CNS HGNET-MN1)”, and CNS high grade neuroepithelial tumour with BCOR alteration (CNS HGNET-BCOR)”.
We describe one such case resembling CNS HGNET-MN1 of a 12 year old boy presenting with a 2 month history of weakness and headache.
Radiological studies showed a left-sided midbrain and pontine tumour with contrast enhacement.
Intraoperative frozen sections showed features of an embryonal tumour. The histology showed an embryonal tumour containing a mixture of solid and pseudopapillary patterns with dense perivascular hyalinization, typical of the pathology seen in astroblastoma in the WHO classification system.
It is noted that most tumours histologically diagnosed as astroblastoma belong to this new molecular entity. Even though the mentioned molecular testing is not locally available, there are morphological aspects of emryonal tumours that may provide clues to molecular subtyping.
Location: Zurich, Switzerland
Southwest Research Institute, USA
Hakima Ibaroudene holds a B.S. and M.S. in Electrical Engineering from the University of Texas in San Antonio. She is a Group Leader in Research Development, a non-profit applied research organization. She has published more than five papers in reputed journals and served as session chair member for the International Telemetry Conference in 2017 and 2018.
We demonstrate an approach to predicting cancer cellularity which uses a combination of weakly- and strongly-labeled data to train a convolutional neural network, where the cellularity scores serve as weak labels and segmentation labels serve as strong labels. By providing segmentation labels for only a small subset of the dataset, we add constraints to the locations of activations at the end of a fully convolutional neural network which predicts cellularity using a global average pool. We utilize a recent neural network architecture, Squeeze-Excitation networks, which reweight feature vectors according to global content in order to refine local predictions. Our method won the BreastPathQ challenge, with our best submission earning an average Pk of .941. Our method is also extremely fast, processing each patch in approximately 19ms, and a whole slide in a matter of minutes