17 citations · 30 across the 6 of their papers we have counts for
6 papers
Rethinking Soft Label in Label Distribution Learning Perspective
Seungbum Hong, Jihun Yoon, Bogyu Park +1
The primary goal of training in early convolutional neural networks (CNN) is the higher generalization performance of the model. However, as the expected calibration error (ECE), w…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?
Arnaud Huaulmé, Kanako Harada, Quang-Minh Nguyen +16
This paper presents the design and results of the "PEg TRAnsfert Workflow recognition" (PETRAW) challenge whose objective was to develop surgical workflow recognition methods based…
hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries
Jihun Yoon, Jiwon Lee, Sunghwan Heo +9
Automated surgical instrument localization is an important technology to understand the surgical process and in order to analyze them to provide meaningful guidance during surgery…
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks
Seungbum Hong, Jihun Yoon, Junmo Kim +1
Knowledge transfer using convolutional neural networks (CNNs) can help efficiently train a CNN with fewer parameters or maximize the generalization performance under limited superv…
Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations
Seungbum Hong, Jiwon Lee, Bokyung Park +5
As the area of application of deep neural networks expands to areas requiring expertise, e.g., in medicine and law, more exquisite annotation processes for expert knowledge trainin…