# Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images

arXiv: https://arxiv.org/abs/1807.02908 · HTML: https://mlsanity.com/paper/1807.02908/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes](https://mlsanity.com/paper/2106.09812/) (2021)
- [Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2403.07939/) (2024)
- [MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering](https://mlsanity.com/paper/2107.03216/) (2021)
- [Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder](https://mlsanity.com/paper/2206.11224/) (2022)
- [Aortic root landmark localization with optimal transport loss for heatmap regression](https://mlsanity.com/paper/2407.04921/) (2024)

## Abstract (arXiv)

Deploying the idea of long-term cumulative return, reinforcement learning has shown remarkable performance in various fields. We propose a formulation of the landmark localization in 3D medical images as a reinforcement learning problem. Whereas value-based methods have been widely used to solve similar problems, we adopt an actor-critic based direct policy search method framed in a temporal difference learning approach. Successful behavior learning is challenging in large state and/or action spaces, requiring many trials. We introduce a partial policy-based reinforcement learning to enable solving the large problem of localization by learning the optimal policy on smaller partial domains. Independent actors efficiently learn the corresponding partial policies, each utilizing their own independent critic. The proposed policy reconstruction from the partial policies ensures a robust and efficient localization utilizing the sub-agents solving simple binary decision problems in their corresponding partial action spaces. The proposed reinforcement learning requires a small number of trials to learn the optimal behavior compared with the original behavior learning scheme.