Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports

Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning. It learns transferable image representations by conducting large-scale fully-supervised or self-supervised learning on a source domain. However, supervised pre-training requires a complex and labor intensive two-stage human-assisted annotation process while self-supervised learning cannot compete with the supervised paradigm. To tackle these issues, we propose a cross-supervised methodology named REviewing FreE-text Reports for Supervision (REFERS), which acquires free supervision signals from original radiology reports accompanying the radiographs. The proposed approach employs a vision transformer and is designed to learn joint representations from multiple views within every patient study. REFERS outperforms its transfer learning and self-supervised learning counterparts on 4 well-known X-ray datasets under extremely limited supervision. Moreover, REFERS even surpasses methods based on a source domain of radiographs with human-assisted structured labels. Thus REFERS has the potential to replace canonical pre-training methodologies.

Broad coverage emotion annotation

In this paper we present the emotion annotation of 1.5 billion words Portuguese corpora, publicly available. We motivate the annotation process and detail the decisions made. The resource is evaluated, being applied to different areas: to study Lusophone literature, to obtain paraphrases, and to do genre comparison.

metID: a R package for automatable compound annotation for LC−MS-based data

Summary Accurate and efficient compound annotation is a long-standing challenge for LC−MS-based data (e.g., untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials. Availability and implementation https://jaspershen.github.io/metID. Supplementary information Supplementary data are available at Bioinformatics online.

A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer

Biomarkers identify patient response to therapy. The potential immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS), expressed on regulating T-cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pathology, including the quantification of biomarkers. In this study, we propose a general AI-based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user-friendly tool that can interact with1 other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell-based segmentation/detection to quantify and analyse the trade-offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression.

Measuring the Degree of Divergence when Labeling Tweets in the Electoral Scenario

Analyzing electoral trends in political scenarios using social media with data mining techniques has become popular in recent years. A problem in this field is to reliably annotate data during the short period of electoral campaigns. In this paper, we present a methodology to measure labeling divergence and an exploratory analysis of data related to the 2018 Brazilian Presidential Elections. As a result, we point out some of the main characteristics that lead to a high level of divergence during the annotation process in this domain. Our analysis shows a high degree of divergence mainly in regard to sentiment labels. Also, a significant difference was identified between labels obtained by manual annotation and labels obtained using an automatic annotation approach.

Universal Dependencies for Urdu Noisy Text

In this paper, the process of creating a Dependency Treebank for tweetsin Urdu,a morphologically rich and less-resourced languageis described. The 500 Urdu tweets treebank iscreated by manually annotating the treebank withlemma, POS tags, morphological and syntacticrelations using the Universal Dependencies annotation scheme, adopted to the peculiarities of Urdu social media text. annotation process is evaluated through Inter-annotator agreement for dependency relations and total agreement of 94.5% and resultant weighted Kappa = 0.876was observed. The treebank is evaluated through 10-fold cross validation using Maltparserwith various feature settings. Results show average UAS score of 74%, LAS score of 62.9% and LA score of 69.8%.

Discovering intercultural relations in the digital age with school students. A few Polish-Italian cases

This article briefly presents how the implementation of ICT in cultural institutions has improved education and research. The article emphasizes discovering the activity of Italian artists in Poland, thanks to digitization, which was carried out with particular intensity in the last two decades. Digitization has become a great achievement thanks to the funding of numerous international projects by the European Commission. Special recognition in this action should be given to Rossella Caffo and her team, under whose guidance the plan for the coordination of digitization in Europe and numerous detailed projects were created. In addition to the millions of digitized objects sent to Europeana, the project teams also developed several programs. One of them, MOVIO, software for virtual exhibitions, began to be used in projects with Polish schools. One of the virtual exhibitions was devoted to Elwiro Michał Andriolli, an outstanding artist of Italian origin. The exhibition was prepared on the basis of digitized materials available on Polish websites and in Europeana, where we also found unique French and Lithuanian materials. Unfortunately, no Italian objects were found there. We plan to continue searching for the achievements of Italian artists in Poland, in the CrowdSchool project (Creative Learning at School thanks to a collaborative Crowdsourcing Annotation Process), whose aim is to prepare didactic materials useful in distance learning and intercultural connections. Close cooperation is planned here between a Polish secondary school from Jarosław, interested in architecture, and a Liceo Artistico from Bologna.

Psychiatry Transcript Annotation: Process Study and Improvements

Background: The demand for psychiatry is increasing each year. Limited research has been performed to improve psychiatrist work experience and reduce daily workload using computational methods. There is currently no validated tool or procedure for the mental health transcript annotation process for generating “gold-standard” data. The purpose of this paper was to determine the annotation process for mental health transcripts and how it can be improved to acquire more reliable results considering human factors elements. Method: Three expert clinicians were recruited in this study to evaluate the transcripts. The clinicians were asked to fully annotate two transcripts. An additional five subjects were recruited randomly (aged between 20-40) for this pilot study, which was divided into two phases, phase 1 (annotation without training) and phase 2 (annotation with training) of five transcripts. Kappa statistics were used to measure the inter-rater reliability and accuracy between subjects. Results: The inter-rater reliability between expert clinicians for two transcripts were 0.26 (CI 0.19 to 0.33) and 0.49 (CI 0.42 to 0.57), respectively. In the pilot testing phases, the mean inter-rater reliability between subjects was higher in phase 2 with training transcript (k= 0.35 (CI 0.052 to 0.625)) than in phase 1 without training transcript (k= 0.29 (CI 0.128 to 0.451)). After training, the accuracy percentage among subjects was significantly higher in transcript A (p=0.04) than transcript B (p=0.10). Conclusion: This study focused on understanding the annotation process for mental health transcripts, which will be applied in training machine learning models. Through this exploratory study, the research found appropriate categorical labels that should be included for transcripts annotation, and the importance of training the subjects. Contributions of this case study will help the psychiatric clinicians and researchers in implementing the recommended data collection process to develop a more accurate artificial intelligence model for fully- or semi-automated transcript annotation.

Semi-Automated Machine Learning Video Annotation for Gastroenterologists

A semi-automatic tool for fast and accurate annotation of endoscopic videos utilizing trained object detection models is presented. A novel workflow is implemented and the preliminary results suggest that the annotation process is nearly twice as fast with our novel tool compared to the current state of the art.

metID: A R package for automatable compound annotation for LC−MS-based data

Accurate and efficient compound annotation is a long-standing challenge for LC−MSbased data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.