Search Results for author: CHONG YIN

Found 5 papers, 3 papers with code

Prompting Vision Foundation Models for Pathology Image Analysis

1 code implementation CVPR 2024 CHONG YIN, SiQi Liu, Kaiyang Zhou, Vincent Wai-Sun Wong, Pong C. Yuen

QAP is based on two quantitative attributes namely K-function-based spatial attributes and histogram-based morphological attributes which are aimed for quantitative assessment of tissue states.

cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis

2 code implementations28 Oct 2022 Jiahao Lu, CHONG YIN, Kenny Erleben, Michael Bachmann Nielsen, Sune Darkner

Recently, attempts have been made to reduce annotation requirements in feature-based self-explanatory models for lung nodule diagnosis.

Active Learning Attribute +1

Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis

2 code implementations27 Jun 2022 Jiahao Lu, CHONG YIN, Oswin Krause, Kenny Erleben, Michael Bachmann Nielsen, Sune Darkner

Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge.

Clinical Knowledge Contrastive Learning

Towards Understanding Deep Policy Gradients: A Case Study on PPO

no code implementations CUHK Course IERG5350 2020 Buhua Liu, CHONG YIN

Deep reinforcement learning has shown impressive performance on many decision-making problems, where deep policy gradient algorithms prevail in continuous action space tasks.

Decision Making reinforcement-learning +1

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