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2020 – today
- 2024
[j6]Azar Kazemi
, Ashkan Rasouli-Saravani, Masoumeh Gharib, Tomé Albuquerque, Saeid Eslami, Peter J. Schüffler:
A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes. Comput. Biol. Medicine 173: 108306 (2024)- [c29]Maximilian Fischer, Peter Neher, Peter J. Schüffler, Shuhan Xiao, Silvia Dias Almeida, Constantin Ulrich, Alexander Muckenhuber, Rickmer Braren, Michael Götz, Jens Kleesiek, Marco Nolden, Klaus H. Maier-Hein:
Abstract: Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning. Bildverarbeitung für die Medizin 2024: 158 - [i7]Maximilian Fischer, Peter Neher, Tassilo Wald, Silvia Dias Almeida, Shuhan Xiao, Peter J. Schüffler, Rickmer Braren, Michael Götz, Alexander Muckenhuber, Jens Kleesiek, Marco Nolden, Klaus H. Maier-Hein:
Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution. CoRR abs/2406.12623 (2024) - 2023
- [c28]Maximilian Fischer, Peter Neher, Michael Götz, Shuhan Xiao, Silvia Dias Almeida, Peter J. Schüffler, Alexander Muckenhuber, Rickmer Braren, Jens Kleesiek, Marco Nolden, Klaus H. Maier-Hein:
Abstract: Deep-learning on Lossily Compressed Pathology Images - Adverse Effects for ImageNet Pre-trained Models. Bildverarbeitung für die Medizin 2023: 245 - [c27]Glejdis Shkëmbi, Johanna P. Müller, Zhe Li, Katharina Breininger, Peter J. Schüffler, Bernhard Kainz:
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. DEMI@MICCAI 2023: 11-20 - [c26]Tomé Albuquerque, Mei Ling Fang, Benedikt Wiestler, Claire Delbridge, Maria João M. Vasconcelos, Jaime S. Cardoso, Peter J. Schüffler:
Multimodal Context-Aware Detection of Glioma Biomarkers Using MRI and WSI. MTSAIL/LEAF/AI4Treat/MMMI/REMIA@MICCAI 2023: 157-167 - [c25]Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak, Peter J. Schüffler, Nassir Navab:
DISBELIEVE: Distance Between Client Models Is Very Essential for Effective Local Model Poisoning Attacks. ISIC/Care-AI/MedAGI/DeCaF@MICCAI 2023: 297-310 - [c24]Maximilian Fischer, Peter Neher, Peter J. Schüffler, Shuhan Xiao, Silvia Dias Almeida, Constantin Ulrich, Alexander Muckenhuber, Rickmer Braren, Michael Götz, Jens Kleesiek, Marco Nolden, Klaus H. Maier-Hein:
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning. MLMI@MICCAI (2) 2023: 427-436 - [d1]Frauke Wilm, Christian Ihling, Gabor Mehes, Luigi Terracciano, Chloé Puget, Robert Klopfleisch, Peter J. Schüffler, Marc Aubreville, Andreas Maier, Thomas Mrowiec, Katharina Breininger:
Pan-Tumor T-Lymphocyte Detection Dataset. Zenodo, 2023
- [i6]Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak, Peter J. Schüffler, Nassir Navab:
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks. CoRR abs/2308.07387 (2023) - [i5]Glejdis Shkëmbi, Johanna P. Müller, Zhe Li, Katharina Breininger, Peter J. Schüffler, Bernhard Kainz:
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. CoRR abs/2310.04187 (2023) - 2022
- [c23]Maximilian Fischer, Philipp Schader, Rickmer Braren, Michael Götz, Alexander Muckenhuber, Wilko Weichert, Peter J. Schüffler, Jens Kleesiek, Jonas Scherer, Klaus Kades, Klaus H. Maier-Hein, Marco Nolden:
DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform. Bildverarbeitung für die Medizin 2022: 273-278 - [c22]Maximilian Fischer, Peter Neher, Michael Götz, Shuhan Xiao, Silvia Dias Almeida, Peter J. Schüffler, Alexander Muckenhuber, Rickmer Braren, Jens Kleesiek, Marco Nolden, Klaus H. Maier-Hein:
Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models. MOVI@MICCAI 2022: 73-83 - 2021
- [j5]Peter J. Schüffler, Luke Geneslaw, Dig Vijay Kumar Yarlagadda, Matthew G. Hanna, Jennifer Samboy, Evangelos Stamelos, Chad M. Vanderbilt, John Philip, Marc-Henri Jean, Lorraine Corsale, Allyne Manzo, Neeraj H. G. Paramasivam, John S. Ziegler, Jianjiong Gao, Juan C. Perin, Young Suk Kim, Umeshkumar K. Bhanot, Michael H. A. Roehrl, Orly Ardon, Sarah Chiang, Dilip D. Giri, Carlie S. Sigel, Lee K. Tan, Melissa Murray, Christina Virgo, Christine England, Yukako Yagi, S. Joseph Sirintrapun, David S. Klimstra, Meera R. Hameed, Victor E. Reuter, Thomas J. Fuchs:
Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center. J. Am. Medical Informatics Assoc. 28(9): 1874-1884 (2021) - [j4]Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Hong Zhao, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-Wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Li Yu, Zhihong Liu, Daiqiang Li, Peter J. Schüffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens:
Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images - The ACDC@LungHP Challenge 2019. IEEE J. Biomed. Health Informatics 25(2): 429-440 (2021) - [c21]Georg Prokop, Michael Örtl, Marina Fotteler, Peter J. Schüffler, Johannes Schobel, Walter Swoboda, Jürgen Schlegel, Friederike Liesche-Starnecker:
Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks. ICIMTH 2021: 397-400 - 2020
- [c20]David Joon Ho, Narasimhan P. Agaram, Peter J. Schüffler, Chad M. Vanderbilt, Marc-Henri Jean, Meera R. Hameed, Thomas J. Fuchs:
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment. MICCAI (5) 2020: 540-549 - [i4]David Joon Ho, Narasimhan P. Agaram, Peter J. Schüffler, Chad M. Vanderbilt, Marc-Henri Jean, Meera R. Hameed, Thomas J. Fuchs:
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment. CoRR abs/2007.01383 (2020) - [i3]Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Yang Li, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-Wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Xiangjun Feng, Zhihong Liu, Daiqiang Li, Peter J. Schüffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens:
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images - the ACDC@LungHP Challenge 2019. CoRR abs/2008.09352 (2020)
2010 – 2019
- 2018
- [j3]Gabriele Campanella, Arjun R. Rajanna, Lorraine Corsale, Peter J. Schüffler, Yukako Yagi, Thomas J. Fuchs:
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology. Comput. Medical Imaging Graph. 65: 142-151 (2018) - 2017
- [c19]Gabriele Abbati, Stefan Bauer, Sebastian Winklhofer, Peter J. Schüffler, Ulrike Held, Jakob M. Burgstaller, Johann Steurer, Joachim M. Buhmann:
MRI-Based Surgical Planning for Lumbar Spinal Stenosis. MICCAI (3) 2017: 116-124 - 2016
- [c18]Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler, Thomas J. Fuchs:
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope. CIBB 2016: 42-58 - [c17]Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo, Thomas J. Fuchs:
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations. MLHC 2016: 191-208 - [i2]Stefan Bauer, Nicolas Carion, Peter J. Schüffler, Thomas J. Fuchs, Peter J. Wild, Joachim M. Buhmann:
Multi-Organ Cancer Classification and Survival Analysis. CoRR abs/1606.00897 (2016) - [i1]Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo, Thomas J. Fuchs:
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations. CoRR abs/1608.00842 (2016) - 2015
- [c16]Dwarikanath Mahapatra, Peter J. Schüffler, Frans Vos, Joachim M. Buhmann:
Crohn's disease segmentation from MRI using learned image priors. ISBI 2015: 625-628 - 2014
- [c15]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Jesica Makanyanga, Jaap Stoker, Stuart A. Taylor, Franciscus M. Vos, Joachim M. Buhmann:
Active learning based segmentation of Crohn's disease using principles of visual saliency. ISBI 2014: 226-229 - [c14]Peter J. Schüffler, Dwarikanath Mahapatra, Robiel Naziroglu, Zhang Li, Carl A. J. Puylaert, Rado Andriantsimiavona, Franciscus M. Vos, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor, Joachim M. Buhmann:
Semi-automatic Crohn's Disease Severity Estimation on MR Imaging. ABDI@MICCAI 2014: 128-138 - [c13]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Carl A. J. Puylaert, Jesica C. Makanyanga, Alex Menys, Rado Andriantsimiavona, Jaap Stoker, Stuart A. Taylor, Franciscus M. Vos, Joachim M. Buhmann:
Combining Multiple Expert Annotations Using Semi-supervised Learning and Graph Cuts for Crohn's Disease Segmentation. ABDI@MICCAI 2014: 139-147 - 2013
- [j2]Dwarikanath Mahapatra, Peter Schueffler, Jeroen A. W. Tielbeek, Joachim M. Buhmann, Franciscus M. Vos:
A Supervised Learning Approach for Crohn's Disease Detection Using Higher-Order Image Statistics and a Novel Shape Asymmetry Measure. J. Digit. Imaging 26(5): 920-931 (2013) - [j1]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Jesica Makanyanga, Jaap Stoker, Stuart A. Taylor, Franciscus M. Vos, Joachim M. Buhmann:
Automatic Detection and Segmentation of Crohn's Disease Tissues From Abdominal MRI. IEEE Trans. Medical Imaging 32(12): 2332-2347 (2013) - [c12]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Crohn's disease tissue segmentation from abdominal MRI using semantic information and graph cuts. ISBI 2013: 358-361 - [c11]Dwarikanath Mahapatra, Alexander Vezhnevets, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Weakly supervised semantic segmentation of Crohn's disease tissues from abdominal MRI. ISBI 2013: 844-847 - [c10]Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor, Joachim M. Buhmann:
A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images. Abdominal Imaging 2013: 1-10 - [c9]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Semi-Supervised and Active Learning for Automatic Segmentation of Crohn's Disease. MICCAI (2) 2013: 214-221 - [c8]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Localizing and segmenting Crohn's disease affected regions in abdominal MRI using novel context features. Image Processing 2013: 86693K - [p1]Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann:
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma. Similarity-Based Pattern Analysis and Recognition 2013: 219-245 - 2012
- [c7]Franciscus M. Vos, Jeroen A. W. Tielbeek, Robiel E. Naziroglu, Zhang Li, Peter Schueffler, Dwarikanath Mahapatra, Alexander Wiebel, Cristina Lavini, Joachim M. Buhmann, Hans-Christian Hege, Jaap Stoker, Lucas J. van Vliet:
Computational modeling for assessment of IBD: To be or not to be? EMBC 2012: 3974-3977 - [c6]Dwarikanath Mahapatra, Peter Schueffler, Jeroen A. W. Tielbeek, Joachim M. Buhmann, Franciscus M. Vos:
A Supervised Learning Based Approach to Detect Crohn's Disease in Abdominal MR Volumes. Abdominal Imaging 2012: 97-106 - 2011
- [c5]Peter J. Schüffler, Aydin Ulas, Umberto Castellani, Vittorio Murino:
A Multiple Kernel Learning Algorithm for Cell Nucleus Classification of Renal Cell Carcinoma. ICIAP (1) 2011: 413-422 - [c4]Manuele Bicego, Aydin Ulas, Peter J. Schüffler, Umberto Castellani, Vittorio Murino, André F. T. Martins, Pedro M. Q. Aguiar, Mário A. T. Figueiredo:
Renal Cancer Cell Classification Using Generative Embeddings and Information Theoretic Kernels. PRIB 2011: 75-86 - [c3]Aydin Ulas, Peter J. Schüffler, Manuele Bicego, Umberto Castellani, Vittorio Murino:
Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma. SIMBAD 2011: 77-89 - [c2]Mehmet Gönen, Aydin Ulas, Peter J. Schüffler, Umberto Castellani, Vittorio Murino:
Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma. SIMBAD 2011: 250-260 - 2010
- [c1]Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann:
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma. DAGM-Symposium 2010: 202-211
Coauthor Index
aka: Franciscus M. Vos
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