Anupam Sah
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Anupam Sah has a total of 20 co-authors. Here are the top co-authors by number of shared publications:
- Alexander Duerr
Department of Business and Economics, University of Wuerzburg, 97070 Wuerzburg, Germany
1 shared publication Christina Lillesaar
1 shared publication- Christoph Michael Flath
University of Würzburg, Würzburg, Germany
1 shared publication - Cora Rüdt von Collenberg
Institute of Clinical Neurobiology, University Hospital Würzburg, 97078 Würzburg, Germany
1 shared publication - Corinna Martin
Institute of Clinical Neurobiology, University Hospital Wuerzburg, 97078 Wuerzburg, Germany
1 shared publication - Dennis Segebarth
Institute of Clinical Neurobiology, University Hospital Würzburg, Würzburg, Germany
1 shared publication
Latest Publications
- Open Access
On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
Fluorescent labeling of biomolecules is widely used for bioimage analyses throughout the life sciences. Recent advances in deep learning (DL) have opened new possibilities to scale the image analysis processes through automation. However, the annotation of fluorescent features with a low signal-to-noise ratio is frequently based on subjective criteria. Training on subjective annotations may ultimately lead to biased DL models yielding irreproducible results. An end-to-end analysis process that integrates data annotation, ground truth estimation, and model training can mitigate this risk. To highlight the importance of this integrated process, we compare different DL-based analysis approaches. Based on data from different laboratories, we show that ground truth estimation from multiple human annotators is indispensable to establish objectivity in fluorescent feature annotations. We demonstrate that ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible and transparent bioimage analyses using DL methods.
Open AccessOn the objectivity, reliability, and validity of deep learning enabled bioimage analyses
Fluorescent labeling of biomolecules is widely used for bioimage analyses throughout the life sciences. Recent advances in deep learning (DL) have opened new possibilities to scale the image analysis processes through automation. However, the annotation of fluorescent features with a low signal-to-noise ratio is frequently based on subjective criteria. Training on subjective annotations may ultimately lead to biased DL models yielding irreproducible results. An end-to-end analysis process that integrates data annotation, ground truth estimation, and model training can mitigate this risk. To highlight the importance of this integrated process, we compare different DL-based analysis approaches. Based on data from different laboratories, we show that ground truth estimation from multiple human annotators is indispensable to establish objectivity in fluorescent feature annotations. We demonstrate that ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible and transparent bioimage analyses using DL methods.