DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks

Federated learning is a promising direction to tackle the privacy issues related to sharing patients' sensitive data. Often, federated systems in the medical image analysis domain assume that the participating local clients are \textit{honest}. Several studies report mechanisms through which a set of malicious clients can be introduced that can poison the federated setup, hampering the performance of the global model. To overcome this, robust aggregation methods have been proposed that defend against those attacks. We observe that most of the state-of-the-art robust aggregation methods are heavily dependent on the distance between the parameters or gradients of malicious clients and benign clients, which makes them prone to local model poisoning attacks when the parameters or gradients of malicious and benign clients are close. Leveraging this, we introduce DISBELIEVE, a local model poisoning attack that creates malicious parameters or gradients such that their distance to benign clients' parameters or gradients is low respectively but at the same time their adverse effect on the global model's performance is high. Experiments on three publicly available medical image datasets demonstrate the efficacy of the proposed DISBELIEVE attack as it significantly lowers the performance of the state-of-the-art \textit{robust aggregation} methods for medical image analysis. Furthermore, compared to state-of-the-art local model poisoning attacks, DISBELIEVE attack is also effective on natural images where we observe a severe drop in classification performance of the global model for multi-class classification on benchmark dataset CIFAR-10.Comment: Accepted by MICCAI 2023 - DeCa

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed.Comment: Accepted for MICCAI DEMI Workshop 202

MethMarker: user-friendly design and optimization of gene-specific DNA methylation assays

A software workflow to translate known differentially methylated regions into clinical biomarker

Positivbeispiele Lärmaktionsplanung: Lärmaktionsplanung – Umsetzungsbeispiele in der kommunalen Praxis

In der Broschüre werden ausgewählte Praxisbeispiele von Lärmminderungsmaßnahmen aus der kommunalen Lärmaktionsplanung oder damit eng verzahnter Planungen vorgestellt. Damit erhalten Gemeinden Impulse für ihre eigene Lärmaktionsplanung. Die Projekte werden in Kurzform beschrieben, zahlreiche Querverweise ermöglichen weitergehende Information

Human Behavior in the Context of Continuous Change - An Exploratory Analysis in a Research and Application Center Industry 4.0

The modern world of work is characterized by discontinuity and innovation. Organizations must adapt to continuous change, which makes it crucial to manage organizational knowledge. Learning and forgetting processes are necessary to react successfully to the changes. On the individual level, this means that individuals have to adapt their behavior, which is often well-learned and routinized. This study aims to take a first step toward a more detailed understanding of human behavior in the context of continuous change. For this purpose, an exploratory analysis was conducted on data collected in a Research and Application Center Industry 4.0. The participants had to deal with the continuous change of routine actions in a simulated production environment, which enabled us to measure their adaptation errors. The occurrence of adaptation errors, their dependency on the type of change, and the behavioral patterns are discussed in detail. Implications for further research are derived

Chemical constituents of the root wood of Erythrina sacleuxii and determination of the absolute configuration of suberectin

Phytochemical investigation on the root wood of Erythrina sacleuxii (Leguminosae) led to the isolation of nine secondary metabolites (1-9). Compound 1 was isolated from the genus Erythrina for the first time. The pure compounds were identified on the basis of comprehensive spectroscopic and spectrometric analyses, while their absolute configurations were determined based on chiroptical measurements. Compounds 5 and 6 showed weak antifungal activity against Pyricularia oryzae with MIC values of 20 µg/mL.   Bull. Chem. Soc. Ethiop. 2020, 34(1), 135-140. DOI: https://dx.doi.org/10.4314/bcse.v34i1.1

A new diagnostic algorithm for Burkitt and diffuse large B-cell lymphomas based on the expression of CSE1L and STAT3 and on MYC rearrangement predicts outcome

Background Aggressive mature B-cell non-Hodgkin's lymphomas (BCL) sharing features of Burkitt's lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) (intermediate BL/DLBCL) but deviating with respect to one or more characteristics are increasingly recognized. The limited knowledge about these biologically heterogeneous lymphomas hampers their assignment to a known entity, raising incertitude about optimal treatment approaches. We therefore searched for discriminative, prognostic, and predictive factors for their better characterization. Patients and methods We analyzed 242 cytogenetically defined aggressive mature BCL for differential protein expression. Marker selection was based on recent gene-expression profile studies. Predictive models for diagnosis were established and validated by a different set of lymphomas. Results CSE1L- and inhibitor of DNA binding-3 (ID3)-overexpression was associated with the diagnosis of BL and signal transduction and transcription-3 (STAT3) with DLBCL (P<0.001 for all markers). All three markers were associated with patient outcome in DLBCL. A new algorithm discriminating BL from DLBCL emerged, including the expression of CSE1L, STAT3, and MYC translocation. This ‘new classifier' enabled the identification of patients with intermediate BL/DLBCL who benefited from intensive chemotherapy regimens. Conclusion The proposed algorithm, which is based on markers with reliable staining properties for routine diagnostics, represents a novel valid tool in separating BL from DLBCL. Most interestingly, it allows segregating intermediate BL/DLBCL into groups with different treatment requirement

Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project)

Rationale and Objectives: The objective of this study was to develop and validate a predictive magnetic resonance imaging (MRI) activity score for ileocolonic Crohn disease activity based on both subjective and semiautomatic MRI features. Materials and Methods: An MRI activity score (the “virtual gastrointestinal tract [VIGOR]” score) was developed from 27 validated magnetic resonance enterography datasets, including subjective radiologist observation of mural T2 signal and semiautomatic measurements of bowel wall thickness, excess volume, and dynamic contrast enhancement (initial slope of increase). A second subjective score was developed based on only radiologist observations. For validation, two observers applied both scores and three existing scores to a prospective dataset of 106 patients (59 women, median age 33) with known Crohn disease, using the endoscopic Crohn's Disease Endoscopic Index of Severity (CDEIS) as a reference standard. Results: The VIGOR score (17.1 × initial slope of increase + 0.2 × excess volume + 2.3 × mural T2) and other activity scores all had comparable correlation to the CDEIS scores (observer 1: r = 0.58 and 0.59, and observer 2: r = 0.34–0.40 and 0.43–0.51, respectively). The VIGOR score, however, improved interobserver agreement compared to the other activity scores (intraclass correlation coefficient = 0.81 vs 0.44–0.59). A diagnostic accuracy of 80%–81% was seen for the VIGOR score, similar to the other scores. Conclusions: The VIGOR score achieves comparable accuracy to conventional MRI activity scores, but with significantly improved reproducibility, favoring its use for disease monitoring and therapy evaluation