Purpose For many applications in the field of computer-assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical workflow analysis are a prerequisite. Often machine learning based approaches serve as basis for analyzing the surgical workflow. In general, machine learning algorithms, such as convolutional neural networks (CNN), require large amounts of labeled data. While data is often available in abundance, many tasks in surgical workflow analysis need annotations by domain experts, making it difficult to obtain a sufficient amount of annotations.Methods The aim of using active learning to train a machine learning model is to reduce the annotation effort. Active learning methods determine which unlabeled data points would provide the most information according to some metric, such as prediction uncertainty. Experts will then be asked to only annotate these data points. The model is then retrained with the new data and used to select further data for annotation. Recently, active learning has been applied to CNN by means of Deep Bayesian Networks (DBN). These networks make it possible to assign uncertainties to predictions. In this paper, we present a DBN-based active learning approach adapted for image-based surgical workflow analysis task. Furthermore, by using a recurrent architecture,
Purpose The course of surgical procedures is often unpredictable, making it difficult to estimate the duration of procedures beforehand. This uncertainty makes scheduling surgical procedures a difficult task. A contextaware method that analyses the workflow of an intervention online and automatically predicts the remaining duration would alleviate these problems.As basis for such an estimate, information regarding the current state of the intervention is a requirement.Methods Today, the operating room contains a diverse range of sensors. During laparoscopic interventions, the endoscopic video stream is an ideal source of such information. Extracting quantitative information from the video is challenging though, due to its high dimensionality. Other surgical devices (e.g. insufflator, lights, etc.) provide data streams which are, in contrast to the video stream, more compact and easier to quantify. Though whether such streams offer sufficient information for estimating the duration of surgery is uncertain. In this paper, we propose and compare methods, based on convolutional neural networks, for continuously predicting the duration of laparoscopic interventions based on unlabeled data, such as from endoscopic image and surgical device streams.
Asthma and allergies are major health concerns in which Ig isotype E plays a pivotal role. Ag-bound IgE drives mast cells and basophils into exocytosis, thereby promoting allergic and potentially anaphylactic reactions. The importance of tightly regulated IgE production is underscored by severe immunological conditions in humans with elevated IgE levels. Cytokines direct IgH class-switching to a particular isotype by initiation of germline transcription (GLT) from isotype-specific intronic (I) promoters. The switch to IgE depends on IL-4, which stimulates GLT of the Iε promoter, but is specifically and strongly impaired in Swap-70−/− mice. Although early events in IL-4 signal transduction (i.e., activation of the JAK/STAT6 pathway) do not require SWAP-70, SWAP-70 deficiency results in impaired Iε GLT. The affinity of STAT6 to chromatin is reduced in absence of SWAP-70. Chromatin immunoprecipitation revealed that SWAP-70 binds to Iε and is required for association of STAT6 with Iε. BCL6, known to antagonize STAT6 particularly at Iε, is increased on Iε in absence of SWAP-70. Other promoters bound by BCL6 and STAT6 were found unaffected. We conclude that SWAP-70 controls IgE production through regulation of the antagonistic STAT6 and BCL6 occupancy of Iε. The identification of this mechanism opens new avenues to inhibit allergic reactions triggered by IgE.
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