Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI
By Gabriel Maicas, Gustavo Carneiro, Andrew P. Bradley, Jacinto Carlos Nascimento, Ian David Reid. 2017
109 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Chao Yu
- Jiming Liu
- Shamim Nemati
- Daniel Rueckert
- Ben Glocker
- Konstantinos Kamnitsas
- Bernhard Kainz
- Jonathan Passerat-Palmbach
In Radiology, Nuclear Medicine and Imaging
- Bernhard Kainz
- Ben Glocker
- Daniel Rueckert
- Shamim Nemati
- Wenjia Bai
- Chao Yu
- Jiming Liu
- Arvid Lundervold
Citations in the corpus
- Reinforcement Learning in Healthcare: A Survey (2019, methodology): “lization in 3D CT images. Several researchers also applied DQN methods in 3D medical image registration problems [262], [263], [264], active breast lesion detection from dynamic contrast-enhanced MRI [265], and robust vessel centerline tracing problems in multi-modality 3D medical volumes [266]. Net”
- Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images (2018, methodology): “A similar RL-based attention mechanism is also used for detecting breast lesions in dynamic contrast-enhanced magnetic resonance volumes [27].”
- Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection (2019, unknown): “Because of the recent success of employing Q-learning in several medical imaging applications [5, 9, 25, 26, 28, 29, 30, 31], we adopt in this paper the common strategy of Q-learning based methods as a solution for the RL problem formulation of landmark detection.”
- Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents (2018, methodology): “[7] proposed a similar method for breast lesion detection using actions to control the location and size of the bounding box.”