ENHANCING SENTIMENT LEXICA WITH NEGATION AND MODALITY FOR SENTIMENT ANALYSIS OF TWEETS

Sentiment analysis became one of the core tasks in the field of Natural Language Processing especially with the rise of social media. Public opinion is important for many domains such as commerce, politics, sociology, psychology, or finance. As an important player in social media, Twitter is the most frequently used microblogging platform for public opinion on any topic. In recent years, sentiment analysis in Twitter turned into a recognized shared task challenge. In this thesis, we propose to enhance sentiment lexica with the linguistic notions negation and modality for this challenge. We test the interoperability between various sentiment lexica with each other and with negation and modality and add some Twitter-specific ad-hoc features. The performance of different combinations of these features is analyzed in comprehensive ablation experiments. We participated in two challenges of the International Workshop on Semantic Evaluations (SemEval 2015). Our system performed robustly and reliably in the sentiment classification of tweets task, where it ranked 9th among 40 participants. However, it proved to be the state-of-the-art for measuring degree of sentiment of tweets with figurative language, where it ranked 1st among 35 systems

Semantic segmentation of surgical hyperspectral images under geometric domain shifts

Robust semantic segmentation of intraoperative image data could pave the way for automatic surgical scene understanding and autonomous robotic surgery. Geometric domain shifts, however, although common in real-world open surgeries due to variations in surgical procedures or situs occlusions, remain a topic largely unaddressed in the field. To address this gap in the literature, we (1) present the first analysis of state-of-the-art (SOA) semantic segmentation networks in the presence of geometric out-of-distribution (OOD) data, and (2) address generalizability with a dedicated augmentation technique termed "Organ Transplantation" that we adapted from the general computer vision community. According to a comprehensive validation on six different OOD data sets comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs semantically annotated with 19 classes, we demonstrate a large performance drop of SOA organ segmentation networks applied to geometric OOD data. Surprisingly, this holds true not only for conventional RGB data (drop of Dice similarity coefficient (DSC) by 46 %) but also for HSI data (drop by 45 %), despite the latter's rich information content per pixel. Using our augmentation scheme improves on the SOA DSC by up to 67 % (RGB) and 90 % (HSI) and renders performance on par with in-distribution performance on real OOD test data. The simplicity and effectiveness of our augmentation scheme makes it a valuable network-independent tool for addressing geometric domain shifts in semantic scene segmentation of intraoperative data. Our code and pre-trained models are available at https://github.com/IMSY-DKFZ/htc.Comment: The first two authors (Jan Sellner and Silvia Seidlitz) contributed equally to this pape

Hyperspectral Imaging for the Evaluation of Microcirculatory Tissue Oxygenation and Perfusion Quality in Haemorrhagic Shock: A Porcine Study

Background: The ultimate goal of haemodynamic therapy is to improve microcirculatory tissue and organ perfusion. Hyperspectral imaging (HSI) has the potential to enable noninvasive microcirculatory monitoring at bedside. Methods: HSI (Tivita® Tissue System) measurements of tissue oxygenation, haemoglobin, and water content in the skin (ear) and kidney were evaluated in a double-hit porcine model of major abdominal surgery and haemorrhagic shock. Animals of the control group (n = 7) did not receive any resuscitation regime. The interventional groups were treated exclusively with either crystalloid (n = 8) or continuous norepinephrine infusion (n = 7). Results: Haemorrhagic shock led to a drop in tissue oxygenation parameters in all groups. These correlated with established indirect markers of tissue oxygenation. Fluid therapy restored tissue oxygenation parameters. Skin and kidney measurements correlated well. High dose norepinephrine therapy deteriorated tissue oxygenation. Tissue water content increased both in the skin and the kidney in response to fluid therapy. Conclusions: HSI detected dynamic changes in tissue oxygenation and perfusion quality during shock and was able to indicate resuscitation effectivity. The observed correlation between HSI skin and kidney measurements may offer an estimation of organ oxygenation impairment from skin monitoring. HSI microcirculatory monitoring could open up new opportunities for the guidance of haemodynamic management

HeiPorSPECTRAL - the Heidelberg Porcine HyperSPECTRAL Imaging Dataset of 20 Physiological Organs

Abstract Hyperspectral Imaging (HSI) is a relatively new medical imaging modality that exploits an area of diagnostic potential formerly untouched. Although exploratory translational and clinical studies exist, no surgical HSI datasets are openly accessible to the general scientific community. To address this bottleneck, this publication releases HeiPorSPECTRAL ( https://www.heiporspectral.org ; https://doi.org/10.5281/zenodo.7737674 ), the first annotated high-quality standardized surgical HSI dataset. It comprises 5,758 spectral images acquired with the TIVITA® Tissue and annotated with 20 physiological porcine organs from 8 pigs per organ distributed over a total number of 11 pigs. Each HSI image features a resolution of 480 × 640 pixels acquired over the 500–1000 nm wavelength range. The acquisition protocol has been designed such that the variability of organ spectra as a function of several parameters including the camera angle and the individual can be assessed. A comprehensive technical validation confirmed both the quality of the raw data and the annotations. We envision potential reuse within this dataset, but also its reuse as baseline data for future research questions outside this dataset. Measurement(s) Spectral Reflectance Technology Type(s) Hyperspectral Imaging Sample Characteristic - Organism Sus scrof