4,262 research outputs found
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
The performance of existing supervised neuron segmentation methods is highly
dependent on the number of accurate annotations, especially when applied to
large scale electron microscopy (EM) data. By extracting semantic information
from unlabeled data, self-supervised methods can improve the performance of
downstream tasks, among which the mask image model (MIM) has been widely used
due to its simplicity and effectiveness in recovering original information from
masked images. However, due to the high degree of structural locality in EM
images, as well as the existence of considerable noise, many voxels contain
little discriminative information, making MIM pretraining inefficient on the
neuron segmentation task. To overcome this challenge, we propose a
decision-based MIM that utilizes reinforcement learning (RL) to automatically
search for optimal image masking ratio and masking strategy. Due to the vast
exploration space, using single-agent RL for voxel prediction is impractical.
Therefore, we treat each input patch as an agent with a shared behavior policy,
allowing for multi-agent collaboration. Furthermore, this multi-agent model can
capture dependencies between voxels, which is beneficial for the downstream
segmentation task. Experiments conducted on representative EM datasets
demonstrate that our approach has a significant advantage over alternative
self-supervised methods on the task of neuron segmentation. Code is available
at \url{https://github.com/ydchen0806/dbMiM}.Comment: IJCAI 23 main track pape
Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
Medical image data are often limited due to the expensive acquisition and
annotation process. Hence, training a deep-learning model with only raw data
can easily lead to overfitting. One solution to this problem is to augment the
raw data with various transformations, improving the model's ability to
generalize to new data. However, manually configuring a generic augmentation
combination and parameters for different datasets is non-trivial due to
inconsistent acquisition approaches and data distributions. Therefore,
automatic data augmentation is proposed to learn favorable augmentation
strategies for different datasets while incurring large GPU overhead. To this
end, we present a novel method, called Dynamic Data Augmentation (DDAug), which
is efficient and has negligible computation cost. Our DDAug develops a
hierarchical tree structure to represent various augmentations and utilizes an
efficient Monte-Carlo tree searching algorithm to update, prune, and sample the
tree. As a result, the augmentation pipeline can be optimized for each dataset
automatically. Experiments on multiple Prostate MRI datasets show that our
method outperforms the current state-of-the-art data augmentation strategies
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