no code implementations • 10 Mar 2023 • Hemin Ali Qadir, Younghak Shin, Jacob Bergsland, Ilangko Balasingham
We propose an efficient feature concatenation method for a CNN-based encoder-decoder model without adding complexity to the model.
no code implementations • 20 Feb 2023 • Hemin Ali Qadir, Ilangko Balasingham, Younghak Shin
In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones.
1 code implementation • ICML 2020 • Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang
Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications.
no code implementations • 30 Aug 2019 • Byeongmoon Ji, Hyemin Jung, Jihyeun Yoon, Kyungyul Kim, Younghak Shin
The prediction reliability of neural networks is important in many applications.
no code implementations • 22 Jul 2019 • Hemin Ali Qadir, Younghak Shin, Johannes Solhusvik, Jacob Bergsland, Lars Aabakken, Ilangko Balasingham
Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%.
no code implementations • 27 Jun 2019 • Younghak Shin, Hemin Ali Qadir, Lars Aabakken, Jacob Bergsland, Ilangko Balasingham
Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy.
no code implementations • 27 Jun 2019 • Younghak Shin, Hemin Ali Qadir, Ilangko Balasingham
In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images.