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- Harvard UniversityUS53 papers
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4 papers · 2 filters
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
Zitong Chen, Chau Pham, Siqi Wang +4
Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such a…
Pseudo-Labeling Enhanced by Privileged Information and Its Application to In Situ Sequencing Images
Marzieh Haghighi, Mario C. Cruz, Erin Weisbart +9
Various strategies for label-scarce object detection have been explored by the computer vision research community. These strategies mainly rely on assumptions that are specific to…
k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment
Minkyu Jeon, Hyeonjin Park, Hyunwoo J. Kim +2
The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally…
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…