Haptics-enabled teleoperation for robot-assisted tumor localization
Bibliographic record
Abstract
This paper focuses on the problem of incorporating haptics-enabled teleoperation in minimally invasive tumor localization. Since the stiffness of a tumor is higher than that of the surrounding tissue, it can be identified as a hard nodule when palpated. Using a Tactile Sensing Instrument (TSI) developed at CSTAR, the distributed pressure profiles along the contacting surface can be measured during remote tissue palpation. The tumor can be detected by using a visualization software that creates a color contour map based on the magnitude of the pressure over the palpated area. The accuracy of this method depends on the uniformity of the force applied to the tissue. A haptics-enabled teleoperation system provides the surgeon with the opportunity to feel the interaction force between the instrument and tissue during Minimally Invasive Surgery (MIS). The objective of this research was to assess the feasibility of combining force feedback with tactile feedback in order to increase the overall performance of tumor localization. The teleoperation system used in this work consists of a Mitsubishi PA10 robot as the slave that is remotely controlled (over a dedicated network) through a 7 Degree-Of-Freedom (DOF) haptic interface. A two-channel architecture, along with hybrid impedance control was utilized to form a bilateral teleoperation system in which the master is under force control and the slave is under position control. The experimental results confirm the effectiveness of using force feedback in robot-assisted tactile sensing for tumor detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".