Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses

Dennis Segebarth, Matthias Griebel, Nikolai Stein, Cora R. Von Collenberg, Corinna Martin, Dominik Fiedler, Lucas B. Comeras, Anupam Sah, Victoria Schoeffler, Theresa Lüffe, Alexander Dürr, Rohini Gupta, Manju Sasi, Christina Lillesaar, Maren D. Lange, Ramon O. Tasan, Nicolas Singewald, Hans-Christian Pape, Christoph M. Flath & Robert Blum
Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation,...
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