Machine learning enhanced virtual autopsy
By Shane O'Sullivan, Andreas Holzinger, Kurt Zatloukal, P. Saldiva, Mohammed Imran Sajid, D. Wichmann. 2017
33 citations recorded by the source corpus.
a University of Sao Paulo (USP), Faculty of Medicine, Department of Pathology. São Paulo, SP, Brazil. b Medical University of Graz, Institute for Medical Informatics/Statistics, Holzinger Group. Graz, Austria. c Medical University of Graz, Institute of Pathology. Graz, Austria. d University of de Sao Paulo (USP), Institute of Advanced Studies. São Paulo, SP, Brazil. e Wirral University Teaching Hospital, Department of Upper GI Surgery. Wirral, United Kingdom. f University Hospital Hamburg Eppendorf, Department of Intensive Care. Hamburg, Germany. STATE-OF-THE-ART AND OBJECTIVES
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Heimo Müller
- Kurt Zatloukal
- Bernd Malle
- Andreas Holzinger
- Robert Reihs
- Peter Kieseberg
- Peter M. Roth
- Longo Luca
Citations in the corpus
- Explainable AI: The New 42? (2018, neutral): “Most of all, this would increase acceptance and trust, which is mandatory in safety-critical systems [12], and desirable in many applications (e.g., in medical robotics [19], Ambient Assisted Living [23], Enterprise decision making [4], etc.).”
- Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology (2017, methodology): “An alternative to invasive autopsy is virtual autopsy which is also conducted with scanning and imaging techniques (O’Sullivan et al., 2017).”