Whether and When does Endoscopy Domain Pretraining Make Sense?

Automated endoscopy video analysis is a challenging task in medical computer vision, with the primary objective of assisting surgeons during procedures. The difficulty arises from the complexity of surgical scenes and the lack of a sufficient amount of annotated data. In recent years, large-scale pretraining has shown great success in natural language processing and computer vision communities. These approaches reduce the need for annotated data, which is always a concern in the medical domain. However, most works on endoscopic video understanding use models pretrained on natural images, creating a domain gap between pretraining and finetuning. In this work, we investigate the need for endoscopy domain-specific pretraining based on downstream objectives. To this end, we first collect Endo700k, the largest publicly available corpus of endoscopic images, extracted from nine public Minimally Invasive Surgery (MIS) datasets. Endo700k comprises more than 700,000 unannotated raw images. Next, we introduce EndoViT, an endoscopy pretrained Vision Transformer (ViT). Through ablations, we demonstrate that domain-specific pretraining is particularly beneficial for more complex downstream tasks, such as Action Triplet Detection, and less effective and even unnecessary for simpler tasks, such as Surgical Phase Recognition. We will release both our code and pretrained models upon acceptance to facilitate further research in this direction

Dynamic Scene Graph Representation for Surgical Video

Surgical videos captured from microscopic or endoscopic imaging devices are rich but complex sources of information, depicting different tools and anatomical structures utilized during an extended amount of time. Despite containing crucial workflow information and being commonly recorded in many procedures, usage of surgical videos for automated surgical workflow understanding is still limited. In this work, we exploit scene graphs as a more holistic, semantically meaningful and human-readable way to represent surgical videos while encoding all anatomical structures, tools, and their interactions. To properly evaluate the impact of our solutions, we create a scene graph dataset from semantic segmentations from the CaDIS and CATARACTS datasets. We demonstrate that scene graphs can be leveraged through the use of graph convolutional networks (GCNs) to tackle surgical downstream tasks such as surgical workflow recognition with competitive performance. Moreover, we demonstrate the benefits of surgical scene graphs regarding the explainability and robustness of model decisions, which are crucial in the clinical setting

The Investment Channel of Monetary Policy : Evidence from Norway

We investigate the transmission of monetary policy to investment using Norwegian administrative data. We have two main findings. First, financially constrained firms are more responsive to monetary policy, but the effect is modest; suggesting that firm heterogeneity plays a minor role in monetary transmission. Second, we disentangle the investment channel of monetary policy into direct effects from interest rate changes and indirect general equilibrium effects. We find that the investment channel of monetary policy is due almost exclusively to direct effects. The two results imply that a representative firm framework with investment adjustment frictions in most cases provides a sufficiently detailed description of the investment channel of monetary policy.publishedVersio

Global loss of imprinting leads to widespread tumorigenesis in adult mice

SummaryLoss of imprinting (LOI), commonly observed in human tumors, refers to loss of monoallelic gene regulation normally conferred by parent-of-origin-specific DNA methylation. To test the function of LOI in tumorigenesis, we developed a model by using transient demethylation to generate imprint-free mouse embryonic stem cells (IF-ES cells). Embryonic fibroblasts derived from IF-ES cells (IF-MEFs) display TGFβ resistance and reduced p19 and p53 expression and form tumors in SCID mice. IF-MEFs exhibit spontaneous immortalization and cooperate with H-Ras in cellular transformation. Chimeric animals derived from IF-ES cells develop multiple tumors arising from the injected IF-ES cells within 12 months. These data demonstrate that LOI alone can predispose cells to tumorigenesis and identify a pathway through which immortality conferred by LOI lowers the threshold for transformation

Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning

Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the microscopic particles' environment presents unique challenges, such as Brownian motion at sufficiently small length-scales. In this work, we explore the role of temperature in the emergence and efficacy of strategies in MARL systems using particle-based Langevin molecular dynamics simulations as a realistic representation of micro-scale environments. To this end, we perform experiments on two different multi-agent tasks in microscopic environments at different temperatures, detecting the source of a concentration gradient and rotation of a rod. We find that at higher temperatures, the RL agents identify new strategies for achieving these tasks, highlighting the importance of understanding this regime and providing insight into optimal training strategies for bridging the generalization gap between simulation and reality. We also introduce a novel Python package for studying microscopic agents using reinforcement learning (RL) to accompany our results.Comment: 12 pages, 5 figure

Target-dependent enrichment of virions determines the reduction of high-throughput sequencing in virus discovery

Viral infections cause many different diseases stemming both from well-characterized viral pathogens but also from emerging viruses, and the search for novel viruses continues to be of great importance. High-throughput sequencing is an important technology for this purpose. However, viral nucleic acids often constitute a minute proportion of the total genetic material in a sample from infected tissue. Techniques to enrich viral targets in high-throughput sequencing have been reported, but the sensitivity of such methods is not well established. This study compares different library preparation techniques targeting both DNA and RNA with and without virion enrichment. By optimizing the selection of intact virus particles, both by physical and enzymatic approaches, we assessed the effectiveness of the specific enrichment of viral sequences as compared to non-enriched sample preparations by selectively looking for and counting read sequences obtained from shotgun sequencing. Using shotgun sequencing of total DNA or RNA, viral targets were detected at concentrations corresponding to the predicted level, providing a foundation for estimating the effectiveness of virion enrichment. Virion enrichment typically produced a 1000-fold increase in the proportion of DNA virus sequences. For RNA virions the gain was less pronounced with a maximum 13-fold increase. This enrichment varied between the different sample concentrations, with no clear trend. Despite that less sequencing was required to identify target sequences, it was not evident from our data that a lower detection level was achieved by virion enrichment compared to shotgun sequencing

Delineating disorder-general and disorder-specific dimensions of psychopathology from functional brain networks in a developmental clinical sample

The interplay between functional brain network maturation and psychopathology during development remains elusive. To establish the structure of psychopathology and its neurobiological mechanisms, mapping of both shared and unique functional connectivity patterns across developmental clinical populations is needed. We investigated shared associations between resting-state functional connectivity and psychopathology in children and adolescents aged 5–21 (n =1689). Specifically, we used partial least squares (PLS) to identify latent variables (LV) between connectivity and both symptom scores and diagnostic information. We also investigated associations between connectivity and each diagnosis specifically, controlling for other diagnosis categories. PLS identified five significant LVs between connectivity and symptoms, mapping onto the psychopathology hierarchy. The first LV resembled a general psychopathology factor, followed by dimensions of internalising- externalising, neurodevelopment, somatic complaints, and thought problems. Another PLS with diagnostic data revealed one significant LV, resembling a cross-diagnostic case-control pattern. The diagnosis-specific PLS identified a unique connectivity pattern for autism spectrum disorder (ASD). All LVs were associated with distinct patterns of functional connectivity. These dimensions largely replicated in an independent sample (n = 420) from the same dataset, as well as to an independent cohort (n =3504). This suggests that covariance in developmental functional brain networks supports transdiagnostic dimensions of psychopathology.publishedVersio