Telerobotic palpation for tumor localization with depth estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it