Search Results for author: Suvranu De

Found 7 papers, 1 papers with code

Cognitive-Motor Integration in Assessing Bimanual Motor Skills

no code implementations16 Apr 2024 Erim Yanik, Xavier Intes, Suvranu De

Accurate assessment of bimanual motor skills is essential across various professions, yet, traditional methods often rely on subjective assessments or focus solely on motor actions, overlooking the integral role of cognitive processes.

Decision Making

One-shot skill assessment in high-stakes domains with limited data via meta learning

1 code implementation16 Dec 2022 Erim Yanik, Steven Schwaitzberg, Gene Yang, Xavier Intes, Jack Norfleet, Matthew Hackett, Suvranu De

This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.

Domain Adaptation Meta-Learning +1

A deep learning model for burn depth classification using ultrasound imaging

no code implementations29 Mar 2022 Sangrock Lee, Rahul, James Lukan, Tatiana Boyko, Kateryna Zelenova, Basiel Makled, Conner Parsey, Jack Norfleet, Suvranu De

The network first learns a low-dimensional manifold of the unburned skin images using an encoder-decoder architecture that reconstructs it from ultrasound images of burned skin.

Decoder Specificity

Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning

no code implementations17 Mar 2022 Erim Yanik, Uwe Kruger, Xavier Intes, Rahul Rahul, Suvranu De

To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated - none of which is currently achievable.

Deep Learning in fNIRS: A review

no code implementations31 Jan 2022 Condell Eastmond, Aseem Subedi, Suvranu De, Xavier Intes

Results: Of the 63 papers considered in this review, 32 report a comparative study of deep learning techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of classification accuracy.

Brain Computer Interface Classification +1

Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review

no code implementations3 Mar 2021 Erim Yanik, Xavier Intes, Uwe Kruger, Pingkun Yan, David Miller, Brian Van Voorst, Basiel Makled, Jack Norfleet, Suvranu De

Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency.

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