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Peter J. Schüffler
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2020 – today
- 2024
- [j6]Azar Kazemi
, Ashkan Rasouli-Saravani
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
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
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
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning. MLMI@MICCAI (2) 2023: 427-436 - [d1]Frauke Wilm
Pan-Tumor T-Lymphocyte Detection Dataset. Zenodo, 2023 - [i6]Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak
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
Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models. MOVI@MICCAI 2022: 73-83 - 2021
- [j5]Peter J. Schüffler
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
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
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
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
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
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
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
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
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
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
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
Weakly supervised semantic segmentation of Crohn's disease tissues from abdominal MRI. ISBI 2013: 844-847 - [c10]Peter J. Schüffler
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
Localizing and segmenting Crohn's disease affected regions in abdominal MRI using novel context features. Medical Imaging: Image Processing 2013: 86693K - [p1]Peter J. Schüffler
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
Computational modeling for assessment of IBD: To be or not to be? EMBC 2012: 3974-3977 - [c6]Dwarikanath Mahapatra, Peter Schueffler
A Supervised Learning Based Approach to Detect Crohn's Disease in Abdominal MR Volumes. Abdominal Imaging 2012: 97-106 - 2011
- [c5]Peter J. Schüffler
A Multiple Kernel Learning Algorithm for Cell Nucleus Classification of Renal Cell Carcinoma. ICIAP (1) 2011: 413-422 - [c4]Manuele Bicego, Aydin Ulas
Renal Cancer Cell Classification Using Generative Embeddings and Information Theoretic Kernels. PRIB 2011: 75-86 - [c3]Aydin Ulas
Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma. SIMBAD 2011: 77-89 - [c2]Mehmet Gönen
Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma. SIMBAD 2011: 250-260 - 2010
- [c1]Peter J. Schüffler
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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last updated on 2024-10-07 21:18 CEST by the dblp team
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