FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization

1 code implementation • 17 Sep 2023 • Sejin Park, Taehyung Lee, Yeejin Lee, Byeongkeun Kang

To the best of our knowledge, we are the first to address this task.

Incremental Learning Knowledge Distillation +2

A molecular generative model with genetic algorithm and tree search for cancer samples

no code implementations • 16 Dec 2021 • Sejin Park, Hyunju Lee

FasterGTS is constructed with a genetic algorithm and a Monte Carlo tree search with three deep neural networks: supervised learning, self-trained, and value networks, and it generates anticancer molecules based on the genetic profiles of a cancer sample.

Drug Discovery

Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays

no code implementations • 21 Nov 2018 • Sejin Park, Woochan Hwang, Kyu-Hwan Jung

Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data.

General Reinforcement Learning reinforcement-learning +3

False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs

no code implementations • 26 Jul 2018 • Sejin Park, Woochan Hwang, Kyu Hwan Jung, Joon Beom Seo, Namkug Kim

Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive.