3 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
Domain adaptation using optimal transport for invariant learning using histopathology datasets
Kianoush Falahkheirkhah, Alex Lu, David Alvarez-Melis +1
Histopathology is critical for the diagnosis of many diseases, including cancer. These protocols typically require pathologists to manually evaluate slides under a microscope, whic…