Search Results for author: Cheng Ge

Found 10 papers, 4 papers with code

Solving Minimum-Cost Reach Avoid using Reinforcement Learning

no code implementations29 Oct 2024 Oswin So, Cheng Ge, Chuchu Fan

Current reinforcement-learning methods are unable to directly learn policies that solve the minimum cost reach-avoid problem to minimize cumulative costs subject to the constraints of reaching the goal and avoiding unsafe states, as the structure of this new optimization problem is incompatible with current methods.

MuJoCo reinforcement-learning +1

Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

no code implementations22 Aug 2024 Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Ershuai Wang, Qin Zhou, Ziyan Huang, Pengju Lyu, Jian He, Bo wang

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment.

Computed Tomography (CT) Segmentation

Cost Splitting for Multi-Objective Conflict-Based Search

no code implementations23 Nov 2022 Cheng Ge, Han Zhang, Jiaoyang Li, Sven Koenig

Our theoretical results show that, when combined with either of these two new splitting strategies, MO-CBS maintains its completeness and optimality guarantees.

Multi-Agent Path Finding

Deep Baseline Network for Time Series Modeling and Anomaly Detection

no code implementations10 Sep 2022 Cheng Ge, Xi Chen, Ming Wang, Jin Wang

By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result.

Anomaly Detection Time Series +1

DePS: An improved deep learning model for de novo peptide sequencing

no code implementations16 Mar 2022 Cheng Ge, Yi Lu, Jia Qu, Liangxu Xie, Feng Wang, Hong Zhang, Ren Kong, Shan Chang

De novo peptide sequencing from mass spectrometry data is an important method for protein identification.

de novo peptide sequencing

AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

1 code implementation28 Oct 2020 Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, Xiaoping Yang

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.

Continual Learning Organ Segmentation +2

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