12 citations · 54 across the 17 of their papers we have counts for
17 papers
Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology
Emely Rosbach, Jonas Ammeling, Jonathan Ganz +4
Artificial intelligence (AI)-driven decision support systems can improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts…
Benchmarking Foundation Models for Mitotic Figure Classification
Jonas Ammeling, Jonathan Ganz, Emely Rosbach +4
The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled imag…
Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification
Jonathan Ganz, Jonas Ammeling, Emely Rosbach +4
Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key ide…
When Two Wrongs Don't Make a Right" -- Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology
Emely Rosbach, Jonas Ammeling, Sebastian Krügel +24
Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboratio…
Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology
Emely Rosbach, Jonathan Ganz, Jonas Ammeling +2
Artificial intelligence (AI)-based clinical decision support systems (CDSS) promise to enhance diagnostic accuracy and efficiency in computational pathology. However, human-AI coll…
On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images
Jonathan Ganz, Christian Marzahl, Jonas Ammeling +20
The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker as it is a measure for tumor cell proliferation. However…
Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis
Marc Aubreville, Jonathan Ganz, Jonas Ammeling +2
The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and com…
Re-identification from histopathology images
Jonathan Ganz, Jonas Ammeling, Samir Jabari +2
In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primar…
Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides
Chloé Puget, Jonathan Ganz, Julian Ostermaier +7
Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit…
Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard
Jonas Ammeling, Moritz Hecker, Jonathan Ganz +4
The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not establis…
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
Marc Aubreville, Zhaoya Pan, Matti Sievert +7
The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversamp…
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan +27
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with…
Nuclear Pleomorphism in Canine Cutaneous Mast Cell Tumors: Comparison of Reproducibility and Prognostic Relevance between Estimates, Manual Morphometry and Algorithmic Morphometry
Andreas Haghofer, Eda Parlak, Alexander Bartel +18
Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurem…
Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images
Jonathan Ganz, Karoline Lipnik, Jonas Ammeling +10
Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be…
Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector
Marc Aubreville, Jonathan Ganz, Jonas Ammeling +4
Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognost…
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays
Jonas Ammeling, Lars-Henning Schmidt, Jonathan Ganz +5
Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational…
Automatic and explainable grading of meningiomas from histopathology images
Jonathan Ganz, Tobias Kirsch, Lucas Hoffmann +7
Meningioma is one of the most prevalent brain tumors in adults. To determine its malignancy, it is graded by a pathologist into three grades according to WHO standards. This grade…