Demo Data from SIGIR 2026 / reviewers in the wild / expert
Qi-ian Poon
dblp:63/8368
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Haptics and multimodal interaction · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics
surgical robotics |
0.1 | 1 | 2010 | Miniaturized force-indentation depth sensor for tissue abnormality identification during laparoscopic surgery · ICRA 2010 |
Medical and health informatics › computational pathology
tissue abnormality localization |
0.1 | 1 | 2010 | Miniaturized force-indentation depth sensor for tissue abnormality identification during laparoscopic surgery · ICRA 2010 |
Haptics and multimodal interaction
force sensing |
0.1 | 1 | 2010 | Miniaturized force-indentation depth sensor for tissue abnormality identification during laparoscopic surgery · ICRA 2010 |
Medical and health informatics › surgical robotics
minimally invasive surgery |
0.0 | 1 | 2010 | Miniaturized force-indentation depth sensor for tissue abnormality identification during laparoscopic surgery · ICRA 2010 |
Methods — techniques the papers use, named apart from their topics
fiber optic sensing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Miniaturized force-indentation depth sensor for tissue abnormality identification during laparoscopic surgeryabstractThis paper presents a novel miniaturized force-indentation depth (FID) sensor designed to conduct indentation on soft tissue during minimally invasive surgery. It can intra-operatively aid the surgeon to rapidly identify the tissue abnormalities within the tissue. The FID sensor can measure the indentation depth of a semi-spherical indenter and the tissue reaction force simultaneously. It make use of with fiber optical fiber sensing method measure indentation depth and force and is small enough to fit through a standard trocar port with a diameter of 11 mm. The created FID sensor was calibrated and tested on silicone block simulating soft tissue. The results show that the sensor can measure the indentation depth accurately and also the orientation of the sensor with respect to the tissue surface whilst performing indentation. Hongbin Liu 0001, Jichun Li 0002, Qi-ian Poon, Lakmal D. Seneviratne, Kaspar Althoefer |
ICRA | 3 |