activity
20242026
most citedComparison of fine-tuning strategies for transfer learning in medical image classification

98 citations · 142 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CV2026★ 4 cited

Bio-inspired fine-tuning for selective transfer learning in image classification

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Deep learning has significantly advanced image analysis across diverse domains but often depends on large, annotated datasets for success. Transfer learning addresses this challeng…

cs.CV2025★ 4 cited

Adaptive transfer learning for surgical tool presence detection in laparoscopic videos through gradual freezing fine-tuning

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Minimally invasive surgery can benefit significantly from automated surgical tool detection, enabling advanced analysis and assistance. However, the limited availability of annotat…

cs.RO2025

Affordance-Based Disambiguation of Surgical Instructions for Collaborative Robot-Assisted Surgery

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Effective human-robot collaboration in surgery is affected by the inherent ambiguity of verbal communication. This paper presents a framework for a robotic surgical assistant that…

cs.CV2025

Transfer learning optimization based on evolutionary selective fine tuning

Jacinto Colan, Ana Davila, Yasuhisa Hasegawa

Deep learning has shown substantial progress in image analysis. However, the computational demands of large, fully trained models remain a consideration. Transfer learning offers a…

cs.RO2025

Assessing the Value of Visual Input: A Benchmark of Multimodal Large Language Models for Robotic Path Planning

Jacinto Colan, Ana Davila, Yasuhisa Hasegawa

Large Language Models (LLMs) show potential for enhancing robotic path planning. This paper assesses visual input's utility for multimodal LLMs in such tasks via a comprehensive be…

cs.CL2025

Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Large Language Models (LLMs) have demonstrated significant capabilities in understanding and generating human language, contributing to more natural interactions with complex syste…

cs.RO2025

LLM-based ambiguity detection in natural language instructions for collaborative surgical robots

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Ambiguity in natural language instructions poses significant risks in safety-critical human-robot interaction, particularly in domains such as surgery. To address this, we propose…

cs.RO2025★ 1 cited

Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assistants

Jacinto Colan, Ana Davila, Yutaro Yamada +1

Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex proc…

cs.RO2024★ 13 cited

Voice control interface for surgical robot assistants

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Traditional control interfaces for robotic-assisted minimally invasive surgery impose a significant cognitive load on surgeons. To improve surgical efficiency, surgeon-robot collab…

cs.RO2024★ 2 cited

Embedded Image-to-Image Translation for Efficient Sim-to-Real Transfer in Learning-based Robot-Assisted Soft Manipulation

Jacinto Colan, Keisuke Sugita, Ana Davila +2

Recent advances in robotic learning in simulation have shown impressive results in accelerating learning complex manipulation skills. However, the sim-to-real gap, caused by discre…

cs.RO2024★ 2 cited

A hierarchical framework for collision avoidance in robot-assisted minimally invasive surgery

Jacinto Colan, Ana Davila, Khusniddin Fozilov +1

Minimally invasive surgery (MIS) procedures benefit significantly from robotic systems due to their improved precision and dexterity. However, ensuring safety in these dynamic and…

cs.CV2024★ 98 cited

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

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

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. Despi…

cs.RO2024★ 6 cited

Manipulability maximization in constrained inverse kinematics of surgical robots

Jacinto Colan, Ana Davila, Yasuhisa Hasegawa

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.…

cs.RO2024★ 10 cited

Task segmentation based on transition state clustering for surgical robot assistance

Yutaro Yamada, Jacinto Colan, Ana Davila +1

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…

cs.RO2024★ 2 cited

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

Jacinto Colan, Ana Davila, Yasuhisa Hasegawa

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 ca…