How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks

no code implementations • 4 Apr 2024 • Dongang Wang, Peilin Liu, Hengrui Wang, Heidi Beadnall, Kain Kyle, Linda Ly, Mariano Cabezas, Geng Zhan, Ryan Sullivan, Weidong Cai, Wanli Ouyang, Fernando Calamante, Michael Barnett, Chenyu Wang

This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.

MRI segmentation

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

1 code implementation • 5 Feb 2024 • Shengyi Huang, Quentin Gallouédec, Florian Felten, Antonin Raffin, Rousslan Fernand Julien Dossa, Yanxiao Zhao, Ryan Sullivan, Viktor Makoviychuk, Denys Makoviichuk, Mohamad H. Danesh, Cyril Roumégous, Jiayi Weng, Chufan Chen, Md Masudur Rahman, João G. M. Araújo, Guorui Quan, Daniel Tan, Timo Klein, Rujikorn Charakorn, Mark Towers, Yann Berthelot, Kinal Mehta, Dipam Chakraborty, Arjun KG, Valentin Charraut, Chang Ye, Zichen Liu, Lucas N. Alegre, Alexander Nikulin, Xiao Hu, Tianlin Liu, Jongwook Choi, Brent Yi

As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.

reinforcement-learning Reinforcement Learning (RL)

Gradient Informed Proximal Policy Optimization

1 code implementation • NeurIPS 2023 • Sanghyun Son, Laura Yu Zheng, Ryan Sullivan, Yi-Ling Qiao, Ming C. Lin

We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm.

Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training

no code implementations • 31 Aug 2023 • Lei Bai, Dongang Wang, Michael Barnett, Mariano Cabezas, Weidong Cai, Fernando Calamante, Kain Kyle, Dongnan Liu, Linda Ly, Aria Nguyen, Chun-Chien Shieh, Ryan Sullivan, Hengrui Wang, Geng Zhan, Wanli Ouyang, Chenyu Wang

Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction.

Federated Learning Lesion Segmentation

Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments

no code implementations • 14 May 2022 • Ryan Sullivan, J. K. Terry, Benjamin Black, John P. Dickerson

Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques.

reinforcement-learning Reinforcement Learning (RL)

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning

no code implementations • 3 May 2022 • Dongnan Liu, Mariano Cabezas, Dongang Wang, Zihao Tang, Lei Bai, Geng Zhan, Yuling Luo, Kain Kyle, Linda Ly, James Yu, Chun-Chien Shieh, Aria Nguyen, Ettikan Kandasamy Karuppiah, Ryan Sullivan, Fernando Calamante, Michael Barnett, Wanli Ouyang, Weidong Cai, Chenyu Wang

In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.

Federated Learning Lesion Segmentation +1

PettingZoo: Gym for Multi-Agent Reinforcement Learning

2 code implementations • NeurIPS 2021 • J. K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis Santos, Rodrigo Perez, Caroline Horsch, Clemens Dieffendahl, Niall L. Williams, Yashas Lokesh, Praveen Ravi

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model.

Multi-agent Reinforcement Learning reinforcement-learning +1