1 citations · 1 across the 2 of their papers we have counts for
5 papers
Seamless Whole Slide Label-Free Virtual Staining
Dou Hoon Kwark, Kianoush Falahkheirkhah, Ji-hun Oh +3
Label-free virtual staining offers a compelling, non-destructive alternative to standard histopathology; however, its clinical adoption is hindered by the computational bottlenecks…
Anatomy of a failure: When, how, and why deep vision fails in scientific domains
Ji-Hun Oh, Dou Hoon Kwark, Kianoush Falahkheirkhah +4
Mirroring its ubiquity in popular media and all human activities, the use of deep learning (DL) is rapidly growing in scientific imaging modalities. However, unlike everyday RGB pi…
Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging
Ji-Hun Oh, Kianoush Falahkheirkhah, Rohit Bhargava
Label-free chemical imaging holds significant promise for improving digital pathology workflows, but data acquisition speed remains a limiting factor. To address this gap, we propo…
Hallucination Detection in Virtually-Stained Histology: A Latent Space Baseline
Ji-Hun Oh, Kianoush Falahkheirkhah, John Cheville +1
Histopathologic analysis of stained tissue remains central to biomedical research and clinical care. Virtual staining (VS) offers a promising alternative, with potential to reduce…
Are We Ready for Out-of-Distribution Detection in Digital Pathology?
Ji-Hun Oh, Kianoush Falahkheirkhah, Rohit Bhargava
The detection of semantic and covariate out-of-distribution (OOD) examples is a critical yet overlooked challenge in digital pathology (DP). Recently, substantial insight and metho…