COOD:Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1% FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities

Engineering solutions to neural stem cell differentiation challenges

Background Damage dealt to the central nervous system (CNS) caused by trauma or disease can have detrimental effects on human quality of life because the CNS has limited regenerative capabilities. Efforts to replace lost neural cells require improved knowledge and methods for differentiation of neural stem cells (NSCs).Objective In this thesis, I aim to chart our current scientific knowledge and progression of neural differentiation and explore practically the feasibility of continuous ultrasound (US) stimulation on neural progenitor cells (NPCs) differentiating into neurons in vitro.Methods I performed a literature study examining previous studies that investigated electrical stimulation, nanoparticles, or ultrasound to improve in vitro or in vivo differentiation of neural stem cells. Using finite element method (FEM) frequency analyses with COMSOL Multiphysics, I investigated the use of a 24-wells plate with piezoelectric lead zirconate titanate [Pb(ZrxTi1–x)O3] (PZT) US transducers. Additionally, I validated in vitro previous findings on the feasibility of differentiation of NSCs to NPCs.Results With the knowledge gained from literature and findings from the experiments, I created a mold for the fabrication of a custom variant of a 24-wells plate made with polydimethylsiloxane (PDMS) to which 2.03 mm thick PZT can be mounted.Conclusion Future research efforts should focus on further developing this technique, specifically into electrical schemes to optimize US transmission to NPCs.Biomedical Engineerin