Search Results - "Schmitt, Max"

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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Portoporator©: A portable low-cost electroporation device for gene transfer to cultured cells in biotechnology, biomedical research and education by Schmitt, Max A., Friedrich, Oliver, Gilbert, Daniel F.

“…Electroporation has been a widely established method for delivering DNA and other material into cells in vitro. Conventional electroporation infrastructure is…”
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Sustainable practices in last-mile logistics of small Brazilian e-retailers: an analysis using an MCDM approach by Bonilla, Maria Alejandra M., Da Silva, Bruna Simões, Schmitt, Max Bogo, Bouzon, Marina

“…Purpose The objective is to contribute to the development of strategies through the identification of sustainability practices. Thus, the research has a…”
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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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Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective by Jutzi, Tanja B., Krieghoff-Henning, Eva I., Holland-Letz, Tim, Utikal, Jochen Sven, Hauschild, Axel, Schadendorf, Dirk, Sondermann, Wiebke, Fröhling, Stefan, Hekler, Achim, Schmitt, Max, Maron, Roman C., Brinker, Titus J.

“…Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these…”
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Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review by Höhn, Julia, Hekler, Achim, Krieghoff-Henning, Eva, Kather, Jakob Nikolas, Utikal, Jochen Sven, Meier, Friedegund, Gellrich, Frank Friedrich, Hauschild, Axel, French, Lars, Schlager, Justin Gabriel, Ghoreschi, Kamran, Wilhelm, Tabea, Kutzner, Heinz, Heppt, Markus, Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Maron, Roman C, Schmitt, Max, Jutzi, Tanja, Fröhling, Stefan, Lipka, Daniel B, Brinker, Titus Josef

“…Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs…”
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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 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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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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Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists by Brinker, Titus J., Schmitt, Max, Krieghoff-Henning, Eva I., Barnhill, Raymond, Beltraminelli, Helmut, Braun, Stephan A., Carr, Richard, Fernandez-Figueras, Maria-Teresa, Ferrara, Gerardo, Fraitag, Sylvie, Gianotti, Raffaele, Llamas-Velasco, Mar, Müller, Cornelia S.L., Perasole, Antonio, Requena, Luis, Sangueza, Omar P., Santonja, Carlos, Starz, Hans, Vale, Esmeralda, Weyers, Wolfgang, Hekler, Achim, Kather, Jakob N., Fröhling, Stefan, Krahl, Dieter, Holland-Letz, Tim, Utikal, Jochen S., Saggini, Andrea, Kutzner, Heinz

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Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study by Schmitt, Max, Maron, Roman Christoph, Hekler, Achim, Stenzinger, Albrecht, Hauschild, Axel, Weichenthal, Michael, Tiemann, Markus, Krahl, Dieter, Kutzner, Heinz, Utikal, Jochen Sven, Haferkamp, Sebastian, Kather, Jakob Nikolas, Klauschen, Frederick, Krieghoff-Henning, Eva, Fröhling, Stefan, von Kalle, Christof, Brinker, Titus Josef

“…An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide…”
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Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study by Maron, Roman C, Hekler, Achim, Krieghoff-Henning, Eva, Schmitt, Max, Schlager, Justin G, Utikal, Jochen S, Brinker, Titus J

“…Studies have shown that artificial intelligence achieves similar or better performance than dermatologists in specific dermoscopic image classification tasks…”
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Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics by Haggenmüller, Sarah, Schmitt, Max, Krieghoff-Henning, Eva, Hekler, Achim, Maron, Roman C, Wies, Christoph, 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, Korsing, Sören, Berking, Carola, Heppt, Markus V, Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Kather, Jakob N, Fröhling, Stefan, Brinker, Titus J

“…The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away…”
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Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images by Marmé, Frederik, Krieghoff-Henning, Eva, Gerber, Bernd, Schmitt, Max, Zahm, Dirk-Michael, Bauerschlag, Dirk, Forstbauer, Helmut, Hildebrandt, Guido, Ataseven, Beyhan, Brodkorb, Tobias, Denkert, Carsten, Stachs, Angrit, Krug, David, Heil, Jörg, Golatta, Michael, Kühn, Thorsten, Nekljudova, Valentina, Gaiser, Timo, Schönmehl, Rebecca, Brochhausen, Christoph, Loibl, Sibylle, Reimer, Toralf, Brinker, Titus J.

“…Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone…”
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Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning by Hekler, Achim, Maron, Roman C., Haggenmüller, Sarah, Schmitt, Max, Wies, Christoph, 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, Korsing, Sören, Berking, Carola, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Kather, Jakob N., Krieghoff-Henning, Eva, Brinker, Titus J.

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Ferroelectricity and Crystal Phases in Mixed‐Cation Lead Iodide Perovskite Solar Cells by Schulz, Alexander Deniz, Schmitt, Max Oliver, Braun, Moritz, Colsmann, Alexander, Hinterstein, Manuel, Röhm, Holger

“…After the discovery of ferroelectricity in archetypal methylammonium lead iodide (MAPbI3), the discussion arose, if more advanced derivatives thereof are also…”
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A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma by Wessels, Frederik, Schmitt, Max, Krieghoff-Henning, Eva, Nientiedt, Malin, Waldbillig, Frank, Neuberger, Manuel, Kriegmair, Maximilian C., Kowalewski, Karl-Friedrich, Worst, Thomas S., Steeg, Matthias, Popovic, Zoran V., Gaiser, Timo, von Kalle, Christof, Utikal, Jochen S., Fröhling, Stefan, Michel, Maurice S., Nuhn, Philipp, Brinker, Titus J.

“…Purpose To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell…”
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Reply to the letter to the editor: ‘Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images’ by Hekler, Achim, Utikal, Jochen S., Solass, Wiebke, Schmitt, Max, Klode, Joachim, Schadendorf, Dirk, Sondermann, Wiebke, Franklin, Cindy, Bestvater, Felix, Krahl, Dieter, von Kalle, Christof, Fröhling, Stefan, Brinker, Titus J.

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Overdiagnosis of melanoma – causes, consequences and solutions by Kutzner, Heinz, Jutzi, Tanja B., Krahl, Dieter, Krieghoff‐Henning, Eva I., Heppt, Markus V., Hekler, Achim, Schmitt, Max, Maron, Roman C. R., Fröhling, Stefan, Kalle, Christof, Brinker, Titus J.

“…Summary Malignant melanoma is the skin tumor that causes most deaths in Germany. At an early stage, melanoma is well treatable, so early detection is…”
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