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Invitation to next week BioVis meetup: </div>
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<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'"><b>BioVis meeup, “Building Explainable AI Tools for Biomedical Applications“,<span class="x_Apple-converted-space"> </span></b></font></div>
<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'"><b>Grace Guo, Harvard University</b></font></div>
<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'">Scheduled: 18. Feb 2026 at 17:00 to 18:00, CET</font></div>
<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'">Location: Join Zoom Meeting</font></div>
<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'">https://tu-dresden.zoom-x.de/j/67863904836?pwd=mVqoyB6DlxAbFAtN7kx0Ka62KHFmUJ.1</font></div>
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<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'">Meeting ID: 678 6390 4836</font></div>
<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'">Passcode: BioV!s2025</font></div>
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<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'">Abstract</font></div>
<div style="margin:0px"><font face="Helvetica Neue" size="3" style="font:12.0px 'Helvetica Neue'">Advances in deep learning have yielded powerful models for diagnosis and prognosis, yet their “black-box” nature continues to stall clinical adoption—especially
in biomedicine, where mistakes have life-changing consequences. Many prior studies have demonstrated how visualizations can be used to provide powerful explanations of generic AI models by translating abstract model mechanics into concrete, perceptual cues.
However, biomedical data presents two key hurdles for explainable AI (XAI). Firstly, it spans diverse modalities: static and dynamic images, multi-channel time-series, genomic sequences, and free-text—all of which must be reconciled in a single, coherent explanation.
Secondly, any explanation must map onto domain concepts that matter to clinicians; generic heat-maps rarely suffice and can even mislead when they are not contextualized in terms of medical expertise or domain knowledge. In this talk, I will present some of
my recent work on domain-centered XAI tools for domain experts such as researchers, doctors, and clinicians. I will also discuss some of the key takeaways from these studies, as well as promising directions of future research leveraging visualizations for
biomedical XAI.</font></div>
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<div><font color="#919191">_________________________</font></div>
<div><font color="#919191">Dr Helena JAMBOR<br>
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National Center for Tumor Diseases,<span class="x_Apple-converted-space"> </span>University Cancer Center, NCT-UCC<br>
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Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden<br>
Fetscherstraße 74, 01307 Dresden</font></div>
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