04/06/2022

France : Post-doctoral research position - Analysis of time series in robotic surgery


Post-doctoral research position

Analysis of time series in robotic surgery

 

Key words: machine learning on time series, clustering, classification, average time series

 

Context

Surgical robotics is now widely used with, for instance, more than 5000 Da Vinci systems and one million procedures performed worldwide. Surgery is a complex activity, in a very small anatomical volume, and with a lot of variability between patients and between surgeons. The global objective of the two-year SPARS (Sequential Pattern Analysis in Robotic Surgery: Understanding Surgery) project led by the MediCIS team (LTSI (1), Inserm, Rennes 1 University) is to develop data analysis approaches being able to provide a better understanding of the surgical practice, from complex surgical data. The approaches will be developed thanks to the complementary skills available in the project’s consortium, including time series analysis. In this consortium, the IRISA laboratory (Rennes and Vannes) is calling for applications for a post-doctoral research position (duration two years) on time series analysis.

(1) Laboratoire Traitement du Signal et de l'Image

 

Missions

In the SPARS project context, a trajectory compiles information on the 3D location of the tip of a surgical instrument at the hands of the surgeon, at a constant frequency. The candidate will be mostly involved in one of the three workpackages of the SPARS project. A first task will focus on clustering and classification for such trajectories. Various practical objectives are pursued, including the generation of a model corresponding to a cluster or a class, the characterization of operating modes specific to a type of patient or a type of surgeon, the provision of advice to practitioners in the case of robotic surgeries that are not or not very well documented, the identification of the level of expertise of a practitioner, the prediction of the surgical procedure to be chosen according to the type of patient. These investigations will use dissimilarity measures based on temporal alignment, as DTW [SC71] or elastic kernels as proposed in [CVB07], [CB17] and [M19a]. This task will also address co-clustering for trajectories. The investigations will focus on how to combine time series with other types of data for a co-clustering purpose, using either deep learning [XCZ19] if enough data is available, symbolic representation [BBC15] or latent block [BLN20] models that all need to be adapted to the specificity of kinematics data.

Once a cluster or a class is obtained, another task will be to compute an average trajectory from a set of trajectories. The practical objectives will be the following: highlight deviations from the average trajectory that are potentially interpretable (as characteristics of the practitioner, or of the patient, for example) ; identify the best operating mode to young practitioners or trainees if it is possible to correlate the operating mode with clinical results. Intuitively, on the graphical representation of a time series, variability related to temporality (phase) concerns the abscissa axis, and variability related to shape concerns the ordinate axis. To compute a consensus trajectory, the second task of the package will examine how to extract the atemporal form and the variable component related to temporality, assuming that this atemporal form may be interpreted as an approximation of the consensus. The problem of shape and phase separation has been studied in [PZ16], [SSV10] and [M19a]. The second task will examine how to improve the preliminary work in [M19b], notably by proposing other kernels.

 

Requirements for this position

Doctorate in computer science, applied mathematics and computer science, or mathematics, with a specialization in machine learning and the following requirements:

  • theoretical skills and experience in probability / statistics, applied mathematics, machine learning,

  • strong knowledge and solid experience in temporal data analysis,

  • publications in major conferences or journals in the field,

  • mastery of data manipulation, relying on machine learning libraries,

  • programming experience, good programming skills (notably in Python) and technical ability to manage a code development project,

  • ability to work in a team, and report on the progress of work.

 

Some knowledge in deep learning will be a plus.

The personal qualities expected are mostly autonomy and interest in interdisciplinarity (health), as well as writing skills (both in French and English). Fluency in French will be a plus.

 

Work environment

Location: Institut de Recherche en informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 - Campus Beaulieu, 263 Av. Général Leclerc, 35000 Rennes

Duration: 24 months – Applications will be accepted until the position is filled (for recruitment by 1 December 2022 at the latest)

Host team: LINKMEDIA

The successful candidate will work with four academic researchers from IRISA / Rennes / LINKMEDIA team (Simon Malinowski, Associate Professor in Computer Science), IRISA / Vannes / EXPRESSION team (Pierre-François Marteau, Full Professor in Computer Science), LS2N (2) / Nantes / DUKe team (Christine Sinoquet, Associate Professor with French Accreditation to supervise Research (HdR)) and INSERM / Rennes / LTSI MediCIS team (Pierre Jannin, Directeur de recherche INSERM, HdR). The successful candidate will collaborate with the partners in the project, among which the other post-doctoral fellow involved in the project and the project partners experts in surgery and in surgical data analysis.

(2) Laboratoire des Sciences du Numérique de Nantes : UMR CNRS 6004

 

Income: 2160,26 euros before taxes monthly

 

How to apply?

 

Documents to be provided :

  • detailed Curriculum Vitae including a complete list of publications,

  • letter of motivation indicating the candidate’s research interests and achievements to date,

  • a selection of publications,

  • the PhD thesis manuscript,

  • Master 2 marks (with rank and number of students in the year)

  • letters of recommendation for the current year,

  • contact details of two referees (at least) with whom the candidate has worked (first name, surname, status, institution (give details of acronyms if applicable), city, e-mail address, telephone number)

 

Questions or application files (zip archive only) should be sent to the four contact persons below:

 

simon.malinowski@irisa.fr

christine.sinoquet@univ-nantes.fr

pierre-francois.marteau@univ-ubs.fr

pierre.jannin@univ-rennes1.fr (SPARS project leader)

 

Simon Malinowksi http://people.irisa.fr/Simon.Malinowski/

Christine Sinoquet https://christinesinoquet.wixsite.com/christinesinoquet

Pierre-François Marteau https://people.irisa.fr/Pierre-Francois.Marteau/

Pierre Jannin https://medicis.univ-rennes1.fr/members/pierre.jannin/index

 

Bibliographical references

[BBC15] A. Bondu, M. Boullé, A. Cornuéjols (2015) Symbolic representation of time series: a hierarchical coclustering formalization. In : International Workshop on Advanced Analysis and Learning on Temporal Data, pp. 3-16.

[BLN20] R. Boutalbi, L. Labiod, M. Nadif (2020) Tensor latent block model for co-clustering. International Journal of Data Science and Analytics, 1-15.

[CVB07] M. Cuturi, J.-P. Vert, O. Birkenes, T. Matsui (2007) A kernel for time series based on global alignments. In: IEEE International Conference on Acoustics, Seepch and Signal Processing, ICAPPS, vol. 2, pp. II–413–II–416.

[CB17] M. Cuturi, M. Blondel (2017) Soft-DTW: a differentiable loss function for time-series. In: International Conference on Machine Learning (ICML), 894-903.

[M19a] P.-F. Marteau (2019) Times series averaging and denoising from a probabilistic perspective on time-elastic kernels. International Journal of Applied Mathematics and Computer Science, 29 (2), 375-392.

[M19b] P.-F. Marteau (2019) On the separation of shape and temporal patterns in time series. Application to signature authentication. https://hal.archives-ouvertes.fr/hal-02373531v2.

[PZ16] V. M. Panaretos, Y. Zemel (2016) Amplitude and phase variation of point processes. The Annals of Statistics, 44(2), 771-812.

[SC71] H. Sakoe, S. Chiba (1971) A dynamic programming approach to continuous speech recognition. In: ICA, Paper 20 CI3.

[SSV10] L. M. Sangalli, P. Secchi, S. Vantini, and V. Vitelli (2010) k-mean alignment for curve clustering. Computational Statistics and Data Analysis, 54(5), 1219-1233.

[XCZ19] D. Xu, W. Cheng, B. Zong et al. (2019) Deep co-clustering. In: SIAM International Conference on Data Mining (SDM), 414-422.