Computational Health Center
Institute of Machine Learning in Biomedical Imaging
The Institute of Machine Learning in Biomedical Imaging (IML) focuses on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Its goal is to transform the use of imaging for diagnostics and prognostics fundamentally. Novel and affordable solutions should empower clinics to make more accurate, fast, and reliable decisions for early detection, treatment planning, and improved patient outcomes. Julia Schnabel’s research focus is on applications in cancer, cardiovascular diseases, and maternal/perinatal health.
For more information on our team, up-to-date research news, and open positions, please visit our website.
The Institute of Machine Learning in Biomedical Imaging (IML) focuses on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Its goal is to transform the use of imaging for diagnostics and prognostics fundamentally. Novel and affordable solutions should empower clinics to make more accurate, fast, and reliable decisions for early detection, treatment planning, and improved patient outcomes. Julia Schnabel’s research focus is on applications in cancer, cardiovascular diseases, and maternal/perinatal health.
For more information on our team, up-to-date research news, and open positions, please visit our website.
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Our Research Groups
Our Team
Director, Institute of Machine Learning in Biomedical Imaging
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Office & Project Management
Our Key Publications
Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, Weicken E, Asselbergs FW, Prior F, Collins GS, Kaissis G, Tsakou G, Buvat I, Kalpathy-Cramer J, Mongan J, Schnabel JA, Kushibar K, Riklund K, Marias K, Amugongo LM, Fromont LS, Maier-Hein L, Cerdá Alberich L, Martí-Bonmatí L, Cardoso MJ, Bobowicz M, Shabani M, Tsiknakis M, Zuluaga MA, Fritzsche M-C, Camacho M, Linguraru MG, Wenzel M, De Bruijne M, Tolsgaard MG, Goisauf M, Cano Abadía M, Papanikolaou N, Lazrak N, Pujol O, Osuala R, Napel S, Joshi S, Klein S, Aussó S, Rogers WA, Puig-Bosch, X, Salahuddin Z, Starmans MPA, and the FUTURE-AI Consortium
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcareBercea C, Wiestler B, Rueckert D, Schnabel, JA
Evaluating Normative Representation Learning in Generative AI for Robust Anomaly Detection in Brain ImagingBosch M, Kallin N, Donakonda S, Zhang JD, Wintersteller H, Hegenbarth S, Heim K, Ramirez C, Fürst A, Lattouf EI, Feuerherd M, Chattopadhyay S, Kumpesa N, Griesser V, Hoflack JC, Siebourg-Polster J, Mogler C, Swadling L, Pallett LJ, Meiser P, Manske K, de Almeida GP, Kosinska AD, Sandu I, Schneider A, Steinbacher V, Teng Y, Schnabel J, Theis F, Gehring AJ, Boonstra A, Janssen HLA, Vandenbosch M, Cuypers E, Öllinger R, Engleitner T, Rad R, Steiger K, Oxenius A, Lo W-L, Klepsch V, Baier G, Holzmann B, Maini MK, Heeren R, Murray PJ, Thimme R, Herrmann C, Protzer U, Böttcher JP, Zehn D, Wohlleber D, Lauer GM, Hofmann M, Luangsay S, and Knolle PA
A liver immune rheostat regulates CD8 T cell immunity in chronic HBV infectionFöllmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vázquez Mézquita AJ, Biavati F, Išgum I, Dewey M
Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteriesSpieker V, Eichhorn H, Hammernik K, Rueckert D, Preibisch C, Karampinos D, Schnabel JA
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive ReviewWagner SJ, Reisenbüchler D, West NP, Niehues JN, Zhu J, Foersch S, Veldhuizen GP, Quirke P, Grabsch HI, van den Brandt PA, Hutchins GGA, Richman SD, Yuan T, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Jonnagaddala J, Hawkins NJ, Ward RL, Morton D, Seymour M, Magill L, Nowak M, Hay J, Koelzer VH, Church DN, TransSCOT consortium, Matek C, Geppert C, Peng C, Zhi C, Ouyang X, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Hoffmeister M, Truhn D, Schnabel JA, Boxberg M, Peng T, Kather JN
Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric studyContact Office
Office & Project Management
iml.officespam prevention@helmholtz-munich.de
Building / Room: 35.33, 204