Telerobotic palpation for tumor localization with depth estimation
Bibliographic record
Abstract
This work is aimed at developing a new minimally invasive approach to characterize tissue properties in real time during telerobotic palpation and to localize tissue abnormality while estimating its depth. This method relies on using a minimally invasive probe with a rigidly mounted tactile sensor at the tip to capture the force distribution map and the indentation depth by each tactile element and thereby generating a stiffness map for the palpated tissue. The hybrid impedance control technique is used for this approach to enable the operator to switch between position control and force control and thereby to autonomously obtain the required information from the remote tissue. The operator would then be able to localize tissue abnormality based on the force distribution map, the tissue stiffness map and the indentation depth which are visually presented to him/her in real time. This method also enables the operator to estimate the depth at which the tissue abnormality is located. Our results show that tactile sensing alone may be unable to detect tumors embedded deep inside tissue and may also not be a good alternative for palpation on uneven tissue surfaces.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".