Genital Dysplasia and Immunosuppression: Why Organ-Specific Therapy Is Important

Background Young patients with Crohn's disease (CD) show a high prevalence of human papillomavirus (HPV) which is the main cause of high-grade squamous intraepithelial lesions (HSIL). A major complication for patients undergoing immunocompromising therapy is the development of genital dysplasia. Methods We report the case of a 32-year-old patient with recurrent genital dysplasia under long-term therapy for CD with a focus on different drug-related, immunosuppressive mechanisms. Results Gynecological examination and biopsy revealed high-grade vulvar intraepithelial neoplasia (VIN) positive for HPV 16 treated with laser vaporization. Due to the combination of HPV positivity, intraoperative multilocularity, and CD, follow-up examinations were performed every 6 months. One year later, the patient showed a VIN at a new location and additionally, a cervical intraepithelial neoplasia (CIN), which were surgically treated. Catch-up HPV vaccination was applied accessorily. After the switch from a TNF-α blocker to vedolizumab, which acts as a gut-selective anti-integrin, the subsequent PAP smear, vulvoscopy, and colposcopy showed no more evidence of dysplasia. Conclusions This case report highlights that gut-selective immunosuppression with vedolizumab might be favorable in young HPV-positive patients due to a good side effect profile. Regular screening and HPV vaccination are a mainstay of dysplasia prevention and control. The risk for HPV-associated dysplasia in immunosuppressed patients is highly dependent on the choice of immunosuppressive therapy

Unsupervised domain adaptation for vertebrae detection and identification in 3D CT volumes using a domain sanity loss

A variety of medical computer vision applications analyze 2D slices of computed tomography (CT) scans, whereas axial slices from the body trunk region are usually identified based on their relative position to the spine. A limitation of such systems is that either the correct slices must be extracted manually or labels of the vertebrae are required for each CT scan to develop an automated extraction system. In this paper, we propose an unsupervised domain adaptation (UDA) approach for vertebrae detection and identification based on a novel Domain Sanity Loss (DSL) function. With UDA the model’s knowledge learned on a publicly available (source) data set can be transferred to the target domain without using target labels, where the target domain is defined by the specific setup (CT modality, study protocols, applied pre- and processing) at the point of use (e.g., a specific clinic with its specific CT study protocols). With our approach, a model is trained on the source and target data set in parallel. The model optimizes a supervised loss for labeled samples from the source domain and the DSL loss function based on domain-specific “sanity checks” for samples from the unlabeled target domain. Without using labels from the target domain, we are able to identify vertebra centroids with an accuracy of 72.8%. By adding only ten target labels during training the accuracy increases to 89.2%, which is on par with the current state-of-the-art for full supervised learning, while using about 20 times less labels. Thus, our model can be used to extract 2D slices from 3D CT scans on arbitrary data sets fully automatically without requiring an extensive labeling effort, contributing to the clinical adoption of medical imaging by hospitals

A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications

​© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.When applying deep learning methods in an industrial vision application, they often fall short of the performance shown in a clean and controlled lab environment due to data quality issues. Few would consider the actual labels as a driving factor, yet inaccurate label data can impair model performance significantly. However, being able to mitigate inaccurate or incomplete labels might also be a cost-saver for real-world projects. Here, we survey state-of-the-art deep learning approaches to resolve such missing labels, noisy labels, and partially labeled data in the prospect of an industrial vision application. We systematically present un-, weakly, and semi-supervised approaches from ’A’ like anomaly detection to ’Z’ like zero-shot classification to resolve these challenges by embracing them

Real world music object recognition

We present solutions to two of the most pressing issues in contemporary optical music recognition (OMR).We improve recognition accuracy on low-quality, real-world (i.e. containing ageing, lighting, or dirt artefacts among others) input data and provide confidence-rated model outputs to enable efficient human post-processing. Specifically, we present (i) a sophisticated input augmentation scheme that can reduce the gap between sanitised benchmarks and realistic tasks through a combination of synthetic data and noisy perturbations of real-world documents; (ii) an adversarial discriminative domain adaptation method that can be employed to improve the performance of OMR systems on low-quality data; (iii) a combination of model ensembles and prediction fusion, which generates trustworthy confidence ratings for each prediction. We evaluate our contributions on a newly created test set consisting of manually annotated pages of varying real-world quality, sourced from International Music Score Library Project (IMSLP) / the Petrucci Music Library. With the presented data augmentation scheme, we achieve a doubling in detection performance from 36.0% to 73.3% on noisy real-world data compared to state-of-the-art training. This result is then combined with robust confidence ratings paving the way forOMR to be deployed in the realworld. Additionally, we showthe merits of unsupervised adversarial domain adaptation for OMR raising the 36.0% baseline to 48.9%. All our code and data are freely available at: https://github.com/raember/s2anet/tree/TISMIR_publication

A Transfer of Idea Approach to the History of Public Administration: The Hybridizations of Administrative Traditions

This chapter provides an overview of the transfer of ideas between German, French, and U.S. Public Administration during the 20th century, based on a wide range of primary sources from the three countries and contests the dominating perspective of path-dependent national silos in Public Administration theory. A largely uncontested assumption persists that the French, German, and U.S. intellectual traditions have followed distinct and separate ideational paths. However, evidence shows that these three classical administrative traditions have experienced significant exchanges and hybridizations. The chapter notably examines the question of the politics-administration dichotomy in the three countries, and offers a reflection on the changing conception of the trias politica across time. Going beyond a comparative perspective, this chapter applies a transfer-of-ideas approach and thus provides a theoretical framework for a transnational analysis of the circulation of administrative ideas. By analyzing the hybridity of administrative traditions in the 20th century, the chapter proposes a new approach to the history of ideas of Public Administration that is also relevant to the contemporary period. Learning from other traditions in no way is a new phenomenon and has happened before New Public Management entered the stage. Intellectual administrative traditions have long been hybrid and open for exogenous ideas