12 citations · 48 across the 21 of their papers we have counts for
22 papers
Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Sweta Banerjee, Alireza Teimoury, Nils Porsche +11
Pathology foundation models (FMs) are models trained on vast amounts of typically unlabeled data and have been shown to yield regularized latent spaces that can be used effectively…
Performance evaluation of deep learning models for image analysis: considerations for visual control and statistical metrics
Christof A. Bertram, Jonas Ammeling, Alexander Bartel +2
Deep learning-based automated image analysis (DL-AIA) has been shown to outperform trained pathologists in tasks related to feature quantification. Related to these capacities the…
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…
Dataset creation for supervised deep learning-based analysis of microscopic images -- review of important considerations and recommendations
Christof A. Bertram, Viktoria Weiss, Jonas Ammeling +6
Supervised deep learning (DL) receives great interest for automated analysis of microscopic images with an increasing body of literature supporting its potential. The development a…
SWAN -- Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces
Sweta Banerjee, Timo Gosch, Sara Hester +11
The annotation of large scale histopathology image datasets remains a major bottleneck in developing robust deep learning models for clinically relevant tasks, such as mitotic figu…
Effortless Vision-Language Model Specialization in Histopathology without Annotation
Jingna Qiu, Nishanth Jain, Jonas Ammeling +2
Recent advances in Vision-Language Models (VLMs) in histopathology, such as CONCH and QuiltNet, have demonstrated impressive zero-shot classification capabilities across various ta…
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…
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation
Sweta Banerjee, Viktoria Weiss, Taryn A. Donovan +9
Atypical mitosis marks a deviation in the cell division process that has been shown be an independent prognostic marker for tumor malignancy. However, atypical mitosis classificati…
Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
Christof A. Bertram, Viktoria Weiss, Taryn A. Donovan +6
Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has be…
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…
Rethinking U-net Skip Connections for Biomedical Image Segmentation
Frauke Wilm, Jonas Ammeling, Mathias Öttl +3
The U-net architecture has significantly impacted deep learning-based segmentation of medical images. Through the integration of long-range skip connections, it facilitated the pre…
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…
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…