Coaching a Robotic Sonographer: Learning Robotic Ultrasound with Sparse Expert's Feedback

• URL: http://arxiv.org/abs/2409.02337v1
• Date: Tue, 3 Sep 2024 23:52:33 GMT
• Title: Coaching a Robotic Sonographer: Learning Robotic Ultrasound with Sparse Expert's Feedback
• Authors: Deepak Raina, Mythra V. Balakuntala, Byung Wook Kim, Juan Wachs, Richard Voyles,
• Abstract summary: This paper proposes a coaching framework for the robotic ultrasound (RUS) to amplify its performance. The framework combines self-supervised practice with sparse expert's feedback through coaching. The coaching by experts is modeled as a Partially Observable Markov Decision Process (POMDP), which updates the policy parameters based on the correction by the expert.
• Score: 2.0831669527081473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound is widely employed for clinical intervention and diagnosis, due to its advantages of offering non-invasive, radiation-free, and real-time imaging. However, the accessibility of this dexterous procedure is limited due to the substantial training and expertise required of operators. The robotic ultrasound (RUS) offers a viable solution to address this limitation; nonetheless, achieving human-level proficiency remains challenging. Learning from demonstrations (LfD) methods have been explored in RUS, which learns the policy prior from a dataset of offline demonstrations to encode the mental model of the expert sonographer. However, active engagement of experts, i.e. Coaching, during the training of RUS has not been explored thus far. Coaching is known for enhancing efficiency and performance in human training. This paper proposes a coaching framework for RUS to amplify its performance. The framework combines DRL (self-supervised practice) with sparse expert's feedback through coaching. The DRL employs an off-policy Soft Actor-Critic (SAC) network, with a reward based on image quality rating. The coaching by experts is modeled as a Partially Observable Markov Decision Process (POMDP), which updates the policy parameters based on the correction by the expert. The validation study on phantoms showed that coaching increases the learning rate by $25\%$ and the number of high-quality image acquisition by $74.5\%$.

Related papers

• CANDERE-COACH: Reinforcement Learning from Noisy Feedback [12.232688822099325]
  The CANDERE-COACH algorithm is capable of learning from noisy feedback by a nonoptimal teacher. We propose a noise-filtering mechanism to de-noise online feedback data, thereby enabling the RL agent to successfully learn with up to 40% of the teacher feedback being incorrect.
  arXiv  Detail & Related papers  (2024-09-23T20:14:12Z)
• MAGDA: Multi-agent guideline-driven diagnostic assistance [43.15066219293877]
  In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists. In this work, we introduce a new approach for zero-shot guideline-driven decision support. We model a system of multiple LLM agents augmented with a contrastive vision-language model that collaborate to reach a patient diagnosis.
  arXiv  Detail & Related papers  (2024-09-10T09:10:30Z)
• RILe: Reinforced Imitation Learning [60.63173816209543]
  RILe is a novel trainer-student system that learns a dynamic reward function based on the student's performance and alignment with expert demonstrations. RILe enables better performance in complex settings where traditional methods falter, outperforming existing methods by 2x in complex simulated robot-locomotion tasks.
  arXiv  Detail & Related papers  (2024-06-12T17:56:31Z)
• Automatic Endoscopic Ultrasound Station Recognition with Limited Data [0.0]
  Pancreatic cancer is a lethal form of cancer that significantly contributes to cancer-related deaths worldwide. Despite advances in medical imaging techniques, pancreatic cancer remains a challenging disease to detect. Endoscopic ultrasound is the most effective diagnostic tool for detecting pancreatic cancer. To obtain complete imaging of the pancreas, practitioners must learn to guide the endoscope into multiple "EUS stations"
  arXiv  Detail & Related papers  (2023-09-21T06:40:05Z)
• A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images [71.26717896083433]
  Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging.
  arXiv  Detail & Related papers  (2023-09-05T19:45:09Z)
• Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot [54.80144694888735]
  We introduce Demonstration-guided EXploration (DEX), an efficient reinforcement learning algorithm. Our method estimates expert-like behaviors with higher values to facilitate productive interactions. Experiments on $10$ surgical manipulation tasks from SurRoL, a comprehensive surgical simulation platform, demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-02-20T05:38:54Z)
• Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System [16.90302469132399]
  We will develop a framework to perform real-time AI-assisted quality assessment and probe position guidance to provide training process for novice learners with less manual intervention.
  arXiv  Detail & Related papers  (2022-12-16T17:59:40Z)
• RL-Based Guidance in Outpatient Hysteroscopy Training: A Feasibility Study [4.614579113754949]
  This work presents an RL-based agent for outpatient hysteroscopy training. Recent advancements enabled performing this type of intervention in the outpatient setup without anaesthesia. While being beneficial to the patient, this approach introduces new challenges for clinicians, who should take additional measures to maintain the level of patient comfort and prevent tissue damage.
  arXiv  Detail & Related papers  (2022-11-26T11:16:17Z)
• Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing [53.096237570992294]
  Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Standardized fidelity assessment is used to measure adherence to treatment principles. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
  arXiv  Detail & Related papers  (2022-09-14T15:33:30Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.