7 research outputs found
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Automatic surgical phase recognition is a challenging and crucial task with
the potential to improve patient safety and become an integral part of
intra-operative decision-support systems. In this paper, we propose, for the
first time in workflow analysis, a Multi-Stage Temporal Convolutional Network
(MS-TCN) that performs hierarchical prediction refinement for surgical phase
recognition. Causal, dilated convolutions allow for a large receptive field and
online inference with smooth predictions even during ambiguous transitions. Our
method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy
videos with and without the use of additional surgical tool information.
Outperforming various state-of-the-art LSTM approaches, we verify the
suitability of the proposed causal MS-TCN for surgical phase recognition.Comment: 10 pages, 2 figure
Anomaly Detection for Application Log Data
In software development, there is an absolute requirement to ensure that a system once developed, functions at its best throughout its lifetime. Application log data is critical to maintaining application performance and thus techniques to parse, understand and detect anomalies in application log data are critical to ensuring efficiency in software development. While initially hampered by limited hardware and lack of quality datasets, anomaly detection techniques have recently received a surge of interest with advancements in machine learning technology and especially neural networks. In this paper, we explore anomaly detection, historical techniques to detect anomalies and recent advancements in neural networks, which promise to revolutionize anomaly detection in application log data. Further, we analyze the most promising anomaly detection techniques and propose a hybrid model combining LSTM Neural Network and Auto Encoder which improves upon existing techniques
KontextsensitivitĂ€t fĂŒr den Operationssaal der Zukunft
The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von groĂer Bedeutung. In dieser Dissertation, die eine der ersten im kĂŒrzlich definierten Bereich âSurgical Data Scienceâ ist, werden drei Themen fĂŒr die automatisierte KontextsensitivitĂ€t im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten âEvent Impact Factorsâ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display
KontextsensitivitĂ€t fĂŒr den Operationssaal der Zukunft
The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von groĂer Bedeutung. In dieser Dissertation, die eine der ersten im kĂŒrzlich definierten Bereich âSurgical Data Scienceâ ist, werden drei Themen fĂŒr die automatisierte KontextsensitivitĂ€t im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten âEvent Impact Factorsâ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display