Comparison of fine-tuning strategies for transfer learning in medical image classification

In the context of medical imaging and machine learning, one of the most pressing challenges is the effective adaptation of pre-trained models to specialized medical contexts. Despite the availability of advanced pre-trained models, their direct application to the highly specialized and diverse field of medical imaging often falls short due to the unique characteristics of medical data. This study provides a comprehensive analysis on the performance of various fine-tuning methods applied to pre-trained models across a spectrum of medical imaging domains, including X-ray, MRI, Histology, Dermoscopy, and Endoscopic surgery. We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. We selected three well-established CNN architectures (ResNet-50, DenseNet-121, and VGG-19) to cover a range of learning and feature extraction scenarios. Although our results indicate that the efficacy of these fine-tuning methods significantly varies depending on both the architecture and the medical imaging type, strategies such as combining Linear Probing with Full Fine-tuning resulted in notable improvements in over 50% of the evaluated cases, demonstrating general effectiveness across medical domains. Moreover, Auto-RGN, which dynamically adjusts learning rates, led to performance enhancements of up to 11% for specific modalities. Additionally, the DenseNet architecture showed more pronounced benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. This work not only provides valuable insights for optimizing pre-trained models in medical image analysis but also suggests the potential for future research into more advanced architectures and fine-tuning methods.Comment: Accepted at Image and Vision Computin

Manipulability maximization in constrained inverse kinematics of surgical robots

In robot-assisted minimally invasive surgery (RMIS), inverse kinematics (IK) must satisfy a remote center of motion (RCM) constraint to prevent tissue damage at the incision point. However, most of existing IK methods do not account for the trade-offs between the RCM constraint and other objectives such as joint limits, task performance and manipulability optimization. This paper presents a novel method for manipulability maximization in constrained IK of surgical robots, which optimizes the robot's dexterity while respecting the RCM constraint and joint limits. Our method uses a hierarchical quadratic programming (HQP) framework that solves a series of quadratic programs with different priority levels. We evaluate our method in simulation on a 6D path tracking task for constrained and unconstrained IK scenarios for redundant kinematic chains. Our results show that our method enhances the manipulability index for all cases, with an important increase of more than 100% when a large number of degrees of freedom are available. The average computation time for solving the IK problems was under 1ms, making it suitable for real-time robot control. Our method offers a novel and effective solution to the constrained IK problem in RMIS applications.Comment: Accepted at 2023 IEEE International Conference on Mechatronics and Automation (ICMA

Constrained Motion Planning for a Robotic Endoscope Holder based on Hierarchical Quadratic Programming

Minimally Invasive Surgeries (MIS) are challenging for surgeons due to the limited field of view and constrained range of motion imposed by narrow access ports. These challenges can be addressed by robot-assisted endoscope systems which provide precise and stabilized positioning, as well as constrained and smooth motion control of the endoscope. In this work, we propose an online hierarchical optimization framework for visual servoing control of the endoscope in MIS. The framework prioritizes maintaining a remote-center-of-motion (RCM) constraint to prevent tissue damage, while a visual tracking task is defined as a secondary task to enable autonomous tracking of visual features of interest. We validated our approach using a 6-DOF Denso VS050 manipulator and achieved optimization solving times under 0.4 ms and maximum RCM deviation of approximately 0.4 mm. Our results demonstrate the effectiveness of the proposed approach in addressing the constrained motion planning challenges of MIS, enabling precise and autonomous endoscope positioning and visual tracking.Comment: Accepted at 2023 International Conference on Control and Robotics Engineering (ICCRE

Sit-to-Stand and Stand-to-Sit Transfer Support for Complete Paraplegic Patients with Robot Suit HAL

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Task segmentation based on transition state clustering for surgical robot assistance

Understanding surgical tasks represents an important challenge for autonomy in surgical robotic systems. To achieve this, we propose an online task segmentation framework that uses hierarchical transition state clustering to activate predefined robot assistance. Our approach involves performing a first clustering on visual features and a subsequent clustering on robot kinematic features for each visual cluster. This enables to capture relevant task transition information on each modality independently. The approach is implemented for a pick-and-place task commonly found in surgical training. The validation of the transition segmentation showed high accuracy and fast computation time. We have integrated the transition recognition module with predefined robot-assisted tool positioning. The complete framework has shown benefits in reducing task completion time and cognitive workload.Comment: Accepted at 2023 International Conference on Control and Robotics Engineering (ICCRE

Learning Algorithm for a Brachiating Robot

This paper introduces a new concept of multi-locomotion robot inspired by an animal. The robot, ‘Gorilla Robot II’, can select the appropriate locomotion (from biped locomotion, quadruped locomotion and brachiation) according to an environment or task. We consider ‘brachiation’ to be one of the most dynamic of animal motions. To develop a brachiation controller, architecture of the hierarchical behaviour-based controller, which consists of behaviour controllers and behaviour coordinators, was used. To achieve better brachiation, an enhanced learning method for motion control, adjusting the timing of the behaviour coordination, is proposed. Finally, it is shown that the developed robot successfully performs two types of brachiation and continuous locomotion.</jats:p

Learning Algorithm for a Brachiating Robot

If charity begins at home, scholarship on the charitable deduction has stayed at home. In the vast legal literature, few authors have engaged the distinction between charitable contributions that are meant to be used within the United States and charitable contributions that are meant to be used abroad. Yet these two types of contributions are treated very differently in the Code and raise very different policy issues. As Americans\u27 giving patterns and the U.S. nonprofit sector grow increasingly international, the distinction will only become more salient. This Article offers the first exploration of how theories of the charitable deduction apply to internationally targeted donations. In so doing, the Article aims to contribute not only to a methodological shift in nonprofit tax scholarship (a strategic remapping), but also to a reappraisal of the deduction literature (an analytic remapping): just as existing theories of the deduction can inform our understanding of foreign charity, considerations of foreign charity can shed light back on the existing theories. I argue that the standard rationales are underdetermined and undertheorized, and propose a new, integrated approach to the charitable deduction. Internationally targeted donations emerge from the analysis holding a strong claim to deductibility – often a stronger claim than domestically targeted donations hold – on almost every relevant dimension, which calls into question current regulations that privilege domestic giving. Oversight and foreign policy concerns, however, complicate the ideal of geographic neutrality and illuminate the charitable deduction\u27s role as an instrument of statecraft. Admitting foreign charity into the debate over the deduction thus changes the debate\u27s terms; it gives deduction theory new urgency as well as new complexity