Ming Tang, Željko Antić, Pedram Fardzadeh, Stefan Pietzsch, Charlotte Schröder, Adrian Eberhardt, Alena van Bömmel, Gabriele Escherich, Winfried Hofmann, Martin A Horstmann, Thomas Illig, J Matt McCrary, Jana Lentes, Markus Metzler, Wolfgang Nejdl, Brigitte Schlegelberger, Martin Schrappe, Martin Zimmermann, Karolina Miarka-Walczyk, Agata Patsorczak, Gunnar Cario, Bernhard Y Renard, Martin Stanulla, Anke Katharina Bergmann
An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia
EBioMedicine
goal acoustic question answering aqa task answer freeform text question content acoustic scene inspired visual question answering vqa task paper based previously introduced clear dataset propose new benchmark aqa namely clear2 emphasizes specific challenges acoustic inputs include handling variable duration scenes scenes built elementary sounds differ training test set also introduce naaqa neural architecture leverages specific properties acoustic inputs use 1d convolutions time frequency process 2d spectrotemporal representations acoustic content shows promising results enables reductions model complexity show time coordinate maps augment temporal localization capabilities enhance performance network 17 percentage points hand frequency coordinate maps little influence task naaqa achieves 795 accuracy aqa task four times fewer parameters previously explored vqa model evaluate performance naaqa independent data set reconstructed daqa also test addition malimo module model clear2 daqa provide detailed analysis results different question types release code produce clear2 well naaqa foster research newly emerging machine learning task