Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

Exploring Patterns of Dynamic Size Changes of Lesions after Hepatic Microwave Ablation in an In Vivo Porcine Model

Microwave ablation (MWA) is a type of minimally invasive cancer therapy that uses heat to induce necrosis in solid tumours. Inter- and post-ablational size changes can influence the accuracy of control imaging, posing a risk of incomplete ablation. The present study aims to explore post-ablation 3D size dynamics in vivo using computed tomography (CT). Ten MWA datasets obtained in nine healthy pigs were used. Lesions were subdivided along the z-axis with an additional planar subdivision into eight subsections. The volume of the subsections was analysed over different time points, subsequently colour-coded and three-dimensionally visualized. A locally weighted polynomial regression model (LOESS) was applied to describe overall size changes, and Student's t-tests were used to assess statistical significance of size changes. The 3D analysis showed heterogeneous volume changes with multiple small changes at the lesion margins over all time points. The changes were pronounced at the upper and lower lesion edges and characterized by initially eccentric, opposite swelling, followed by shrinkage. In the middle parts of the lesion, we observed less dimensional variations over the different time points. LOESS revealed a hyperbolic pattern for the volumetric changes with an initially significant volume increase of 11.6% (111.6% of the original volume) over the first 32 minutes, followed by a continuous decrease to 96% of the original volume (p < 0.05)

Improved Visualization of the Necrotic Zone after Microwave Ablation Using Computed Tomography Volume Perfusion in an In Vivo Porcine Model

After hepatic microwave ablation, the differentiation between fully necrotic and persistent vital tissue through contrast enhanced CT remains a clinical challenge. Therefore, there is a need to evaluate new imaging modalities, such as CT perfusion (CTP) to improve the visualization of coagulation necrosis. MWA and CTP were prospectively performed in five healthy pigs. After the procedure, the pigs were euthanized, and the livers explanted. Orthogonal histological slices of the ablations were stained with a vital stain, digitalized and the necrotic core was segmented. CTP maps were calculated using a dual-input deconvolution algorithm. The segmented necrotic zones were overlaid on the DICOM images to calculate the accuracy of depiction by CECT/CTP compared to the histological reference standard. A receiver operating characteristic analysis was performed to determine the agreement/true positive rate and disagreement/false discovery rate between CECT/CTP and histology. Standard CECT showed a true positive rate of 81% and a false discovery rate of 52% for display of the coagulation necrosis. Using CTP, delineation of the coagulation necrosis could be improved significantly through the display of hepatic blood volume and hepatic arterial blood flow (p < 0.001). The ratios of true positive rate/false discovery rate were 89%/25% and 90%/50% respectively. Other parameter maps showed an inferior performance compared to CECT

Overview of the IWSLT 2017 Evaluation Campaign

The IWSLT 2017 evaluation campaign has organised three tasks. The Multilingual task, which is about training machine translation systems handling many-to-many language directions, including so-called zero-shot directions. The Dialogue task, which calls for the integration of context information in machine translation, in order to resolve anaphoric references that typically occur in human-human dialogue turns. And, finally, the Lecture task, which offers the challenge of automatically transcribing and translating real-life university lectures. Following the tradition of these reports, we will described all tasks in detail and present the results of all runs submitted by their participants

Benchmarking Deep Learning Models for Tooth Structure Segmentation.

A wide range of deep learning (DL) architectures with varying depths are available, with developers usually choosing one or a few of them for their specific task in a nonsystematic way. Benchmarking (i.e., the systematic comparison of state-of-the art architectures on a specific task) may provide guidance in the model development process and may allow developers to make better decisions. However, comprehensive benchmarking has not been performed in dentistry yet. We aimed to benchmark a range of architecture designs for 1 specific, exemplary case: tooth structure segmentation on dental bitewing radiographs. We built 72 models for tooth structure (enamel, dentin, pulp, fillings, crowns) segmentation by combining 6 different DL network architectures (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network, Mask Attention Network) with 12 encoders from 3 different encoder families (ResNet, VGG, DenseNet) of varying depth (e.g., VGG13, VGG16, VGG19). On each model design, 3 initialization strategies (ImageNet, CheXpert, random initialization) were applied, resulting overall into 216 trained models, which were trained up to 200 epochs with the Adam optimizer (learning rate = 0.0001) and a batch size of 32. Our data set consisted of 1,625 human-annotated dental bitewing radiographs. We used a 5-fold cross-validation scheme and quantified model performances primarily by the F1-score. Initialization with ImageNet or CheXpert weights significantly outperformed random initialization (P < 0.05). Deeper and more complex models did not necessarily perform better than less complex alternatives. VGG-based models were more robust across model configurations, while more complex models (e.g., from the ResNet family) achieved peak performances. In conclusion, initializing models with pretrained weights may be recommended when training models for dental radiographic analysis. Less complex model architectures may be competitive alternatives if computational resources and training time are restricting factors. Models developed and found superior on nondental data sets may not show this behavior for dental domain-specific tasks

Comparing different deep learning architectures for classification of chest radiographs

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse and 14 with corpuls cpr. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression

Evaluation of potential tissue heating during percutaneous drill-assisted bone sampling in an in vivo porcine study

Background: Minimally invasive, battery-powered drilling systems have become the preferred tool for obtaining representative samples from bone lesions. However, the heat generated during battery-powered bone drilling for bone biopsies has not yet been sufficiently investigated. Thermal necrosis can occur if the bone temperature exceeds a critical threshold for a certain period of time. Purpose: To investigate heat production as a function of femur temperature during and after battery-powered percutaneous bone drilling in a porcine in vivo model. Methods: We performed 16 femur drillings in 13 domestic pigs with an average age of 22 weeks and an average body temperature of 39.7 degrees C, using a battery-powered drilling system and an intraosseous temperature monitoring device. The standardized duration of the drilling procedure was 20 s. The bone core specimens obtained were embedded in 4% formalin, stained with haematoxylin and eosin (H&E) and sent for pathological analysis of tissue quality and signs of thermal damage. Results: No significant changes in the pigs' local temperature were observed after bone drilling with a battery-powered drill device. Across all measurements, the median change in temperature between the initial measurement and the temperature measured after drilling (at 20 s) was 0.1 degrees C. Histological examination of the bone core specimens revealed no signs of mechanical or thermal damage. Conclusion: Overall, this preliminary study shows that battery-powered, drill-assisted harvesting of bone core specimens does not appear to cause mechanical or thermal damage