output
20192024
most citedUnderstanding metric-related pitfalls in image analysis validation

204 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

6 papers

q-bio.OT2024★ 9 cited

Enabling Global Image Data Sharing in the Life Sciences

Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner +20

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we tak…

q-bio.OT2024★ 2 cited

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data

Nikki Bialy, Frank Alber, Brenda Andrews +44

Alongside molecular insights into genes and proteins, biological imaging holds great promise for deepening scientific understanding of complex cellular systems and advancing predic…

q-bio.OT2023★ 87 cited

Community-developed checklists for publishing images and image analysis

Christopher Schmied, Michael Nelson, Sergiy Avilov +51

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. H…

cs.CV2023★ 204 cited

Understanding metric-related pitfalls in image analysis validation

Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…

q-bio.OT2021★ 63 cited

QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy

Glyn Nelson, Ulrike Boehm, Steve Bagley +97

In April 2020, the QUality Assessment and REProducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scie…

cs.CV2019★ 2 cited

Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation

Peixian Liang, Jianxu Chen, Hao Zheng +3

The Encoder-Decoder architecture is a main stream deep learning model for biomedical image segmentation. The encoder fully compresses the input and generates encoded features, and…