output
20192026
most citedThe reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study

179 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
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2025★ 1 cited

Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohort

Peshawa J. Muhammad Ali, Navin Vincent, Saman S. Abdulla +8

Background: Artificial intelligence (AI) is improving the efficiency and accuracy of cancer diagnostics. The performance of pathology AI systems has been almost exclusively evaluat…

cs.CV2025

Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry

Anders Blilie, Nita Mulliqi, Xiaoyi Ji +13

Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign…

cs.CV2025★ 3 cited

Foundation Models -- A Panacea for Artificial Intelligence in Pathology?

Nita Mulliqi, Anders Blilie, Xiaoyi Ji +28

The role of artificial intelligence (AI) in pathology has evolved from aiding diagnostics to uncovering predictive morphological patterns in whole slide images (WSIs). Recently, fo…

cs.CV2025★ 1 cited

Two-Stream Thermal Imaging Fusion for Enhanced Time of Birth Detection in Neonatal Care

Jorge García-Torres, Øyvind Meinich-Bache, Sara Brunner +3

Around 10% of newborns require some help to initiate breathing, and 5\% need ventilation assistance. Accurate Time of Birth (ToB) documentation is essential for optimizing neonatal…

cs.CV2024★ 7 cited

Advancing Newborn Care: Precise Birth Time Detection Using AI-Driven Thermal Imaging with Adaptive Normalization

Jorge García-Torres, Øyvind Meinich-Bache, Anders Johannessen +3

Around 5-10\% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and opportunities for learning from…

cs.CV2024★ 4 cited

Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images

Saul Fuster, Farbod Khoraminia, Julio Silva-Rodríguez +7

We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated pro…