331 citations · 361 across the 9 of their papers we have counts for
9 papers
OCELOT 2023: Cell Detection from Cell-Tissue Interaction Challenge
JaeWoong Shin, Jeongun Ryu, Aaron Valero Puche +21
Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular d…
SCORPION: Addressing Scanner-Induced Variability in Histopathology
Jeongun Ryu, Heon Song, Seungeun Lee +4
Ensuring reliable model performance across diverse domains is a critical challenge in computational pathology. A particular source of variability in Whole-Slide Images is introduce…
Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer
Biagio Brattoli, Mohammad Mostafavi, Taebum Lee +14
Despite advancements in methodologies, immunohistochemistry (IHC) remains the most utilized ancillary test for histopathologic and companion diagnostics in targeted therapies. Howe…
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Jeongun Ryu, Aaron Valero Puche, JaeWoong Shin +9
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection,…
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
Inwan Yoo, Donggeun Yoo, Kyunghyun Paeng
Nuclei segmentation is one of the important tasks for whole slide image analysis in digital pathology. With the drastic advance of deep learning, recent deep networks have demonstr…
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
Byungjae Lee, Kyunghyun Paeng
Predicting TNM stage is the major determinant of breast cancer prognosis and treatment. The essential part of TNM stage classification is whether the cancer has metastasized to the…
Action-Driven Object Detection with Top-Down Visual Attentions
Donggeun Yoo, Sunggyun Park, Kyunghyun Paeng +2
A dominant paradigm for deep learning based object detection relies on a "bottom-up" approach using "passive" scoring of class agnostic proposals. These approaches are efficient bu…
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park +1
We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images. Our system offers a fully automated solution to predicting both…