Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI

Deep learning-based diagnostic performance increases with more annotated data, but large-scale manual annotations are expensive and labour-intensive. Experts evaluate diagnostic images during clinical routine, and write their findings in reports. Leveraging unlabelled exams paired with clinical reports could overcome the manual labelling bottleneck. We hypothesise that detection models can be trained semi-supervised with automatic annotations generated using model predictions, guided by sparse information from clinical reports. To demonstrate efficacy, we train clinically significant prostate cancer (csPCa) segmentation models, where automatic annotations are guided by the number of clinically significant findings in the radiology reports. We included 7,756 prostate MRI examinations, of which 3,050 were manually annotated. We evaluated prostate cancer detection performance on 300 exams from an external centre with histopathology-confirmed ground truth. Semi-supervised training improved patient-based diagnostic area under the receiver operating characteristic curve from $87.2 \pm 0.8\%$ to $89.4 \pm 1.0\%$ ($P<10^{-4}$) and improved lesion-based sensitivity at one false positive per case from $76.4 \pm 3.8\%$ to $83.6 \pm 2.3\%$ ($P<10^{-4}$). Semi-supervised training was 14$\times$ more annotation-efficient for case-based performance and 6$\times$ more annotation-efficient for lesion-based performance. This improved performance demonstrates the feasibility of our training procedure. Source code is publicly available at github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCa detection algorithm is available at grand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/

Radiative transfer in disc galaxies I - A comparison of four methods to solve the transfer equation in plane-parallel geometry

Accurate photometric and kinematic modelling of disc galaxies requires the inclusion of radiative transfer models. Due to the complexity of the radiative transfer equation (RTE), sophisticated techniques are required. Various techniques have been employed for the attenuation in disc galaxies, but a quantitative comparison of them is difficult, because of the differing assumptions, approximations and accuracy requirements which are adopted in the literature. In this paper, we present an unbiased comparison of four methods to solve the RTE, in terms of accuracy, efficiency and flexibility. We apply them all on one problem that can serve as a first approximation of large portions of disc galaxies: a one-dimensional plane-parallel geometry, with both absorption and multiple scattering taken into account, with an arbitrary vertical distributions of stars and dust and an arbitrary angular redistribution of the scattering. We find that the spherical harmonics method is by far the most efficient way to solve the RTE, whereas both Monte Carlo simulations and the iteration method, which are straightforward to extend to more complex geometries, have a cost which is about 170 times larger.Comment: 12 pages, 4 figures, accepted for publication in MNRA

Kinematics of elliptical galaxies with a diffuse dust component - III. A Monte Carlo approach to include the effects of scattering

This paper is the third one in a series, intended to investigate how the observed kinematics of elliptical galaxies are affected by dust attenuation. In Paper I and Paper II, we investigated the effects of dust absorption; here we extend our modelling in order to include the effects of scattering. We describe how kinematical information can be combined with the radiative transfer equation, and present a Monte Carlo code that can handle kinematical information in an elegant way. Compared to the case where only absorption is taken into account, we find that dust attenuation considerably affects the observed kinematics when scattering is included. For the central lines of sight, dust can either decrease or increase the central observed velocity dispersion. The most important effect of dust attenuation, however, is found at large projected radii. The kinematics at these lines of sight are strongly affected by photons scattered into these lines of sight, which were emitted by high-velocity stars in the central regions of the galaxy. These photons bias the LOSVDs towards high line-of-sight velocities, and significantly increase the observed velocity dispersion and LOSVD shape parameters. These effects are similar to the expected kinematical signature of a dark matter halo, such that dust attenuation may form an alternative explanation for the usual stellar kinematical evidence for dark matter halos around elliptical galaxies. We apply our results to discuss several other topics in galactic dynamics, where we feel dust attenuation should be taken into account. In particular, we argue that the kinematics observed at various wavelengths can help to constrain the spatial distribution of dust in elliptical galaxies.Comment: 21 pages, 10 figures, accepted for publication in MNRA

Intranasal Delivery of Influenza Subunit Vaccine Formulated with GEM Particles as an Adjuvant

Nasal administration of influenza vaccine has the potential to facilitate influenza control and prevention. However, when administered intranasally (i.n.), commercially available inactivated vaccines only generate systemic and mucosal immune responses if strong adjuvants are used, which are often associated with safety problems. We describe the successful use of a safe adjuvant Gram-positive enhancer matrix (GEM) particles derived from the food-grade bacterium Lactococcus lactis for i.n. vaccination with subunit influenza vaccine in mice. It is shown that simple admixing of the vaccine with the GEM particles results in a strongly enhanced immune response. Already after one booster, the i.n. delivered GEM subunit vaccine resulted in hemagglutination inhibition titers in serum at a level equal to the conventional intramuscular (i.m.) route. Moreover, i.n. immunization with GEM subunit vaccine elicited superior mucosal and Th1 skewed immune responses compared to those induced by i.m. and i.n. administered subunit vaccine alone. In conclusion, GEM particles act as a potent adjuvant for i.n. influenza immunization

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges