Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning

Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and an external validation set show that our approach yields higher accuracy while reducing model size.Comment: multiple-instance learning; self-supervised learning; semi-supervised learning; medical imagin

Short-Term Effects of Ketamine and Isoflurane on Left Ventricular Ejection Fraction in an Experimental Swine Model

Background. General anesthesia is an essential element of experimental medical procedures. Ketamine and isoflurane are agents commonly used to induce and maintain anesthesia in animals. The cardiovascular effects of these anesthetic agents are diverse, and the response of global myocardial function is unknown. Methods. In a series of 15 swine, echocardiography measurements of left ventricular ejection fraction (LVEF) were obtained before the animals received anesthesia (baseline), after an intramuscular injection of ketamine (postketamine) and after inhaled isoflurane (postisoflurane). Results. The mean LVEF of an unanesthetized swine was 47 ± 3%. There was a significant decrease in the mean LVEF after administration of ketamine to 41 + 6.5% (P = 0.003). The addition of inhaled isoflurane did not result in further decrease in mean LVEF (mean LVEF 38 ± 7.2%, P = 0.22). Eight of the swine had an increase in their LVEF with sympathetic stimulation. Conclusions. In our experimental model the administration of ketamine was associated with decreased LV function. The decrease may be largely secondary to a blunting of sympathetic tone. The addition of isoflurane to ketamine did not significantly change LV function. A significant number of animals had returned to preanesthesia LV function with sympathetic stimulation

Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a new deep learning framework for automatic interpretation for structural heart diseases like AS. When deployed, SMMIL can combine information from two input modalities, spectral Dopplers and 2D cineloops, to produce a study-level AS diagnosis. During training, SMMIL can combine a smaller labeled set and an abundant unlabeled set of both modalities to improve its classifier. Experiments demonstrate that SMMIL outperforms recent alternatives at 3-level AS severity classification as well as several clinically relevant AS detection tasks.Comment: Echocardiography; Multimodal; Semi-supervised Learning; Multiple-Instance Learnin

Special Issue “H. pylori Virulence Factors in the Induction of Gastric Cancer”

Twenty-five years ago, Helicobacter pylori was identified as the causative agent of gastric disorders,ranging from acute inflammation [...

How Combining Terrorism, Muslim, and Refugee Topics Drives Emotional Tone in Online News: A Six-Country Cross-Cultural Sentiment Analysis

This study looks into how the combination of Islam, refugees, and terrorism topics leads to text-internal changes in the emotional tone of news articles and how these vary across countries and media outlets. Using a multilingual human-validated sentiment analysis, we compare fear and pity in more than 560,000 articles from the most important online news sources in six countries (U.S., Australia, Germany, Switzerland, Turkey, and Lebanon). We observe that fear and pity work antagonistically—that is, the more articles in a particular topical category contain fear, the less pity they will feature. The coverage of refugees without mentioning terrorists and Muslims/Islam featured the lowest fear and highest pity levels of all topical categories studied here. However, when refugees were covered in combination with terrorism and/or Islam, fear increased and pity decreased in Christian-majority countries, whereas no such pattern appeared in Muslim-majority countries (Lebanon, Turkey). Variations in emotions are generally driven more by country-level differences than by the political alignment of individual outlets

Elastic Pion Scattering on the Deuteron in a Multiple Scattering Model

Pion elastic scattering on deuterium is studied in the KMT multiple scattering approach developed in momentum space. Using a Paris wave function and the same methods and approximations as commonly used in pion scattering on heavier nuclei excellent agreement with differential cross section data is obtained for a wide range of pion energies. Only for $T_{\pi}>250$ MeV and very backward angles, discrepancies appear that are reminiscent of disagreements in pion scattering on $^3$He, $^3$H, and $^4$He. At low energies the second order corrections have been included. Polarization observables are studied in detail. While tensor analyzing powers are well reproduced, vector analyzing powers exhibit dramatic discrepancies.Comment: 25 pages LATEX and 9 postscript figures in a self-extracting uufile archiv

Role of acetylcholine and polyspecific cation transporters in serotonin-induced bronchoconstriction in the mouse

BACKGROUND: It has been proposed that serotonin (5-HT)-mediated constriction of the murine trachea is largely dependent on acetylcholine (ACh) released from the epithelium. We recently demonstrated that ACh can be released from non-neuronal cells by corticosteroid-sensitive polyspecific organic cation transporters (OCTs), which are also expressed by airway epithelial cells. Hence, the hypothesis emerged that 5-HT evokes bronchoconstriction by inducing release of ACh from epithelial cells via OCTs. METHODS: We tested this hypothesis by analysing bronchoconstriction in precision-cut murine lung slices using OCT and muscarinic ACh receptor knockout mouse strains. Epithelial ACh content was measured by HPLC, and the tissue distribution of OCT isoforms was determined by immunohistochemistry. RESULTS: Epithelial ACh content was significantly higher in OCT1/2 double-knockout mice (42 ± 10 % of the content of the epithelium-denuded trachea, n = 9) than in wild-type mice (16.8 ± 3.6 %, n = 11). In wild-type mice, 5-HT (1 μM) caused a bronchoconstriction that slightly exceeded that evoked by muscarine (1 μM) in intact bronchi but amounted to only 66% of the response to muscarine after epithelium removal. 5-HT-induced bronchoconstriction was undiminished in M(2)/M(3 )muscarinic ACh receptor double-knockout mice which were entirely unresponsive to muscarine. Corticosterone (1 μM) significantly reduced 5-HT-induced bronchoconstriction in wild-type and OCT1/2 double-knockout mice, but not in OCT3 knockout mice. This effect persisted after removal of the bronchial epithelium. Immunohistochemistry localized OCT3 to the bronchial smooth muscle. CONCLUSION: The doubling of airway epithelial ACh content in OCT1/2(-/- )mice is consistent with the concept that OCT1 and/or 2 mediate ACh release from the respiratory epithelium. This effect, however, does not contribute to 5-HT-induced constriction of murine intrapulmonary bronchi. Instead, this activity involves 1) a non-cholinergic epithelium-dependent component, and 2) direct stimulation of bronchial smooth muscle cells, a response which is partly sensitive to acutely administered corticosterone acting on OCT3. These data provide new insights into the mechanisms involved in 5-HT-induced bronchoconstriction, including novel information about non-genomic, acute effects of corticosteroids on bronchoconstriction