Dr. Mina Jamshidi Idaji
Dr. Mina Jamshidi Idaji
Postdoctoral Researcher
Mina Jamshidi is a postdoctoral researcher working at the machine learning group, TU Berlin. She received her Ph.D. in machine learning from TU Berlin in 2022. She conducted her doctoral research at the Max Planck Institute CBS from 2018-2022. Her research involves using and developing ML methods for biomedical data analysis, including neural data analysis and computational pathology.
- Biomedical Data Analysis
- Computational Pathology
- Neural Data Analysis
- IEEE member
Philipp Keyl, Niklas Kiermeyer, Jonah Bosserhoff, Tim Lenfers, Thibault Niederhauser, Bowen Fan, Thomas Schnake, Simon Schallenberg, Fabio Aubele, Solveig Kuss, Mina Jamshidi Idaji, Philipp Jurmeister, Moon Kim, Sebastian Bauer, Nikolaos Bechrakis, Michael Forsting, Dagmar Führer-Sakel, Martin Glas, Viktor Grünwald, Boris Hadaschik, Ken Herrmann, Stefan Kasper, Rainer Kimmig, Stephan Lang, Ina Pretzell, Tienush Rassaf, Alexander Roesch, Jens T. Siveke, Maja Guberina, Ulrich Sure, Marc Wichert, Michael Ingrisch, Kristian Unger, Jürgen Behr, Daniel Teupser, Christian G. Stief, Julia Mayerle, Nadia Harbeck, Amanda Tufman, Jens Ricke, Lars H. Lindner, Siegfried Priglinger, Günter Höglinger, Sven Mahner, Martin Canis, Lucie Heinzerling, Christine Spitzweg, Alpaslan Tasdogan, Matthias Totzeck, Anja Welt, Marcel Wiesweg, C. Benedikt Westphalen, Reinhard Thasler, Fady Albashiti, Grégoire Montavon, Nicola Miglino, Zsolt Balázs, Michael von Bergwelt-Baildon, Volker Heinemann, Claus Belka, Sylvia Hartmann, Andreas Wicki, Felix Nensa, Dirk Schadendorf, Michael Krauthammer, Klaus-Robert Müller, Martin Schuler, Frederick Klauschen, Jens Kleesiek, Julius Keyl
An explainable transformer model learning from entire treatment timelines for pan-cancer risk profiling across healthcare systems
Yanqing Luo, Julius Hense, Niklas Prenißl, Andreas Mock, Klaus-Robert Müller, Thomas Schnake, Mina Jamshidi Idaji
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Mina Jamshidi Idaji, Julius Hense, Tom Neuhäuser, Augustin Krause, Yanqing Luo, Oliver Eberle, Thomas Schnake, Laure Ciernik, Farnoush Rezaei Jafari, Reza Vahidimajd, Jonas Dippel, Christoph Walz, Frederick Klauschen, Andreas Mock, Klaus-Robert Müller
Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology
Alhuda Dabbagh, Mina Jamshidi Idaji, Elinor Tzvi-Minker, Vadim Nikulin, Jost-Julian Rumpf, Joseph Classen
Absence of Evidence of Post-Training tDCS Effects on Motor Memory Consolidation and Premotor–Primary Motor Cortex Interaction: a resting-state EEG study
Julius Hense, Mina Jamshidi Idaji, Oliver Eberle, Thomas Schnake, Jonas Dippel, Laure Ciernik, Oliver Buchstab, Andreas Mock, Frederick Klauschen, Klaus-Robert Müller
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Beyond the Snapshot: New AI takes a dynamic view of Cancer Diagnosis
A consortium led by Dr. Mina Jamshidi Idaji (BIFOLD/TU Berlin), with LMU Munich and UKE Hamburg, is uniting oncologists, pathologists and ML researchers to build AI that combines tissue images with molecular and clinical data. Funded with €1.5M over 3 years, starting Oct 1, 2026.