Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study

no code implementations • 25 Jan 2024 • Lukas Heinlein, Roman C. Maron, Achim Hekler, Sarah Haggenmüller, Christoph Wies, Jochen S. Utikal, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Lars E. French, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Eva Krieghoff-Henning, Titus J. Brinker

Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis.

Melanoma Diagnosis Specificity

Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study

no code implementations • 5 Jun 2023 • Achim Hekler, Roman C. Maron, Sarah Haggenmüller, Max Schmitt, Christoph Wies, Jochen S. Utikal, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Lars E. French, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Jakob N. Kather, Eva Krieghoff-Henning, Titus J. Brinker

Classifier performance was measured using area under the receiver operating characteristic curve (AUROC), expected calibration error (ECE) and maximum confidence change (MCC) for (I) a single-view scenario, (II) a multiview scenario using multiple artificially modified images per lesion and (III) a multiview scenario with multiple real-world images per lesion.

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

no code implementations • 17 Mar 2023 • Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher, Carina Nogueira Garcia, Christoph Wies, Harald Kittler, Philipp Tschandl, Cristian Navarrete-Dechent, Sebastian Podlipnik, Emmanouil Chousakos, Iva Crnaric, Jovana Majstorovic, Linda Alhajwan, Tanya Foreman, Sandra Peternel, Sergei Sarap, İrem Özdemir, Raymond L. Barnhill, Mar Llamas Velasco, Gabriela Poch, Sören Korsing, Wiebke Sondermann, Frank Friedrich Gellrich, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Matthias Goebeler, Bastian Schilling, Jochen S. Utikal, Kamran Ghoreschi, Stefan Fröhling, Eva Krieghoff-Henning, Titus J. Brinker

To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1

Topology of Privacy: Lattice Structures and Information Bubbles for Inference and Obfuscation

no code implementations • 12 Dec 2017 • Michael Erdmann

An element in the lattice consists of two components, one being a set of individuals, the other being a set of attributes.

Combinatorics Cryptography and Security 06A15, 55U05, 68T37