Search Results - "Utikal, Jochen S."

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images by Hekler, Achim, Utikal, Jochen S., Enk, Alexander H., Solass, Wiebke, Schmitt, Max, Klode, Joachim, Schadendorf, Dirk, Sondermann, Wiebke, Franklin, Cindy, Bestvater, Felix, Flaig, Michael J., Krahl, Dieter, von Kalle, Christof, Fröhling, Stefan, Brinker, Titus J.

“…The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high…”
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Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark by Brinker, Titus J., Hekler, Achim, Hauschild, Axel, Berking, Carola, Schilling, Bastian, Enk, Alexander H., Haferkamp, Sebastian, Karoglan, Ante, von Kalle, Christof, Weichenthal, Michael, Sattler, Elke, Schadendorf, Dirk, Gaiser, Maria R., Klode, Joachim, Utikal, Jochen S.

“…Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified…”
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IL-6 regulates CCR5 expression and immunosuppressive capacity of MDSC in murine melanoma by Weber, Rebekka, Riester, Zeno, Hüser, Laura, Sticht, Carsten, Siebenmorgen, Alina, Groth, Christopher, Hu, Xiaoying, Altevogt, Peter, Utikal, Jochen S, Umansky, Viktor

“…BackgroundMyeloid-derived suppressor cells (MDSC) play a major role in the immunosuppressive melanoma microenvironment. They are generated under chronic…”
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Deep learning can predict survival directly from histology in clear cell renal cell carcinoma by Wessels, Frederik, Schmitt, Max, Krieghoff-Henning, Eva, Kather, Jakob N, Nientiedt, Malin, Kriegmair, Maximilian C, Worst, Thomas S, Neuberger, Manuel, Steeg, Matthias, Popovic, Zoran V, Gaiser, Timo, von Kalle, Christof, Utikal, Jochen S, Fröhling, Stefan, Michel, Maurice S, Nuhn, Philipp, Brinker, Titus J

“…For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial…”
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The "Intimarzt" (intimate doctor) Model Project: Anonymous Remote Diagnosis of Sexually Transmitted Diseases by Sondermann, Wiebke, Wies, Christoph, Esser, Stefan, Utikal, Jochen S, Schadendorf, Dirk, Brinker, Titus J

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Case report: Therapeutic potential of T-VEC in combination with MEK inhibitors in melanoma patients with NRAS mutation by Simon, Sonja C S, Müller, Verena, Utikal, Jochen S

“…Mutations in the gene are common alterations in malignant melanoma. However, there are no specific treatment options approved for -mutated melanoma patients…”
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Generation of two hiPSC lines from a patient with autism spectrum disorder harboring a 120 kb deletion in SHANK2 and two control lines from each parent by Cristian, Flavia-Bianca, Köppel, Alexandra, Janssen, Johannes, Utikal, Jochen S., Rappold, Gudrun A., Berkel, Simone

“…Two human induced pluripotent stem cell lines (hiPSC) were generated by reprogramming fibroblasts isolated from a skin biopsy taken from a female patient…”
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Effects of Label Noise on Deep Learning-Based Skin Cancer Classification by Hekler, Achim, Kather, Jakob N, Krieghoff-Henning, Eva, Utikal, Jochen S, Meier, Friedegund, Gellrich, Frank F, Upmeier Zu Belzen, Julius, French, Lars, Schlager, Justin G, Ghoreschi, Kamran, Wilhelm, Tabea, Kutzner, Heinz, Berking, Carola, Heppt, Markus V, Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Izar, Benjamin, Maron, Roman, Schmitt, Max, Fröhling, Stefan, Lipka, Daniel B, Brinker, Titus J

“…Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin…”
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Deep neural networks are superior to dermatologists in melanoma image classification by Brinker, Titus J., Hekler, Achim, Enk, Alexander H., Berking, Carola, Haferkamp, Sebastian, Hauschild, Axel, Weichenthal, Michael, Klode, Joachim, Schadendorf, Dirk, Holland-Letz, Tim, von Kalle, Christof, Fröhling, Stefan, Schilling, Bastian, Utikal, Jochen S.

“…Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable…”
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Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review by Kuntz, Sara, Krieghoff-Henning, Eva, Kather, Jakob N., Jutzi, Tanja, Höhn, Julia, Kiehl, Lennard, Hekler, Achim, Alwers, Elizabeth, von Kalle, Christof, Fröhling, Stefan, Utikal, Jochen S., Brenner, Hermann, Hoffmeister, Michael, Brinker, Titus J.

“…Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial…”
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Cutaneous, gastrointestinal, hepatic, endocrine, and renal side-effects of anti-PD-1 therapy by Hofmann, Lars, Forschner, Andrea, Loquai, Carmen, Goldinger, Simone M, Zimmer, Lisa, Ugurel, Selma, Schmidgen, Maria I, Gutzmer, Ralf, Utikal, Jochen S, Göppner, Daniela, Hassel, Jessica C, Meier, Friedegund, Tietze, Julia K, Thomas, Ioannis, Weishaupt, Carsten, Leverkus, Martin, Wahl, Renate, Dietrich, Ursula, Garbe, Claus, Kirchberger, Michael C, Eigentler, Thomas, Berking, Carola, Gesierich, Anja, Krackhardt, Angela M, Schadendorf, Dirk, Schuler, Gerold, Dummer, Reinhard, Heinzerling, Lucie M

“…Abstract Background Anti-programmed cell death receptor-1 (PD-1) antibodies represent an effective treatment option for metastatic melanoma as well as for…”
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Explainable artificial intelligence in skin cancer recognition: A systematic review by Hauser, Katja, Kurz, Alexander, Haggenmüller, Sarah, Maron, Roman C., von Kalle, Christof, Utikal, Jochen S., Meier, Friedegund, Hobelsberger, Sarah, Gellrich, Frank F., Sergon, Mildred, Hauschild, Axel, French, Lars E., Heinzerling, Lucie, Schlager, Justin G., Ghoreschi, Kamran, Schlaak, Max, Hilke, Franz J., Poch, Gabriela, Kutzner, Heinz, Berking, Carola, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Kather, Jakob N., Fröhling, Stefan, Lipka, Daniel B., Hekler, Achim, Krieghoff-Henning, Eva, Brinker, Titus J.

“…Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making…”
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Ipilimumab alone or in combination with nivolumab after progression on anti-PD-1 therapy in advanced melanoma by Zimmer, Lisa, Apuri, Susmitha, Eroglu, Zeynep, Kottschade, Lisa A, Forschner, Andrea, Gutzmer, Ralf, Schlaak, Max, Heinzerling, Lucie, Krackhardt, Angela M, Loquai, Carmen, Markovic, Svetomir N, Joseph, Richard W, Markey, Kelly, Utikal, Jochen S, Weishaupt, Carsten, Goldinger, Simone M, Sondak, Vernon K, Zager, Jonathan S, Schadendorf, Dirk, Khushalani, Nikhil I

“…Abstract Background The anti-programmed cell death-1 (PD-1) inhibitors pembrolizumab and nivolumab alone or in combination with ipilimumab have shown improved…”
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Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification by Höhn, Julia, Krieghoff-Henning, Eva, Jutzi, Tanja B., von Kalle, Christof, Utikal, Jochen S., Meier, Friedegund, Gellrich, Frank F., Hobelsberger, Sarah, Hauschild, Axel, Schlager, Justin G., French, Lars, Heinzerling, Lucie, Schlaak, Max, Ghoreschi, Kamran, Hilke, Franz J., Poch, Gabriela, Kutzner, Heinz, Heppt, Markus V., Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Goebeler, Matthias, Hekler, Achim, Fröhling, Stefan, Lipka, Daniel B., Kather, Jakob N., Krahl, Dieter, Ferrara, Gerardo, Haggenmüller, Sarah, Brinker, Titus J.

“…Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. We investigated whether a…”
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Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer by Wessels, Frederik, Schmitt, Max, Krieghoff‐Henning, Eva, Jutzi, Tanja, Worst, Thomas S., Waldbillig, Frank, Neuberger, Manuel, Maron, Roman C., Steeg, Matthias, Gaiser, Timo, Hekler, Achim, Utikal, Jochen S., Kalle, Christof, Fröhling, Stefan, Michel, Maurice S., Nuhn, Philipp, Brinker, Titus J.

“…Objective To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node…”
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Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study by Maron, Roman C, Utikal, Jochen S, Hekler, Achim, Hauschild, Axel, Sattler, Elke, Sondermann, Wiebke, Haferkamp, Sebastian, Schilling, Bastian, Heppt, Markus V, Jansen, Philipp, Reinholz, Markus, Franklin, Cindy, Schmitt, Laurenz, Hartmann, Daniela, Krieghoff-Henning, Eva, Schmitt, Max, Weichenthal, Michael, von Kalle, Christof, Fröhling, Stefan, Brinker, Titus J

“…Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence…”
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A benchmark for neural network robustness in skin cancer classification by Maron, Roman C., Schlager, Justin G., Haggenmüller, Sarah, von Kalle, Christof, Utikal, Jochen S., Meier, Friedegund, Gellrich, Frank F., Hobelsberger, Sarah, Hauschild, Axel, French, Lars, Heinzerling, Lucie, Schlaak, Max, Ghoreschi, Kamran, Hilke, Franz J., Poch, Gabriela, Heppt, Markus V., Berking, Carola, Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Goebeler, Matthias, Krieghoff-Henning, Eva, Hekler, Achim, Fröhling, Stefan, Lipka, Daniel B., Kather, Jakob N., Brinker, Titus J.

“…One prominent application for deep learning–based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often…”
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Multimodal integration of image, epigenetic and clinical data to predict BRAF mutation status in melanoma by Schneider, Lucas, Wies, Christoph, Krieghoff-Henning, Eva I., Bucher, Tabea-Clara, Utikal, Jochen S., Schadendorf, Dirk, Brinker, Titus J.

“…In machine learning, multimodal classifiers can provide more generalised performance than unimodal classifiers. In clinical practice, physicians usually also…”
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Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours by Brinker, Titus J., Kiehl, Lennard, Schmitt, Max, Jutzi, Tanja B., Krieghoff-Henning, Eva I., Krahl, Dieter, Kutzner, Heinz, Gholam, Patrick, Haferkamp, Sebastian, Klode, Joachim, Schadendorf, Dirk, Hekler, Achim, Fröhling, Stefan, Kather, Jakob N., Haggenmüller, Sarah, von Kalle, Christof, Heppt, Markus, Hilke, Franz, Ghoreschi, Kamran, Tiemann, Markus, Wehkamp, Ulrike, Hauschild, Axel, Weichenthal, Michael, Utikal, Jochen S.

“…Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this…”
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Dimethyl fumarate treatment in relapsed and refractory cutaneous T-cell lymphoma: a multicenter phase 2 study by Nicolay, Jan P, Melchers, Susanne, Albrecht, Jana D, Assaf, Chalid, Dippel, Edgar, Stadler, Rudolf, Wehkamp, Ulrike, Wobser, Marion, Zhao, Jing, Burghaus, Ina, Schneider, Sven, Gülow, Karsten, Goerdt, Sergij, Schürch, Christian M, Utikal, Jochen S, Krammer, Peter H

“…Targeted therapies for cutaneous T-cell lymphoma (CTCL) are limited and curative approaches are lacking. Furthermore, relapses and drug induced side effects…”
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