Uncertainty Quantification in Biophotonic Imaging using Invertible Neural Networks

Owing to high stakes in the field of healthcare, medical machine learning (ML) applications have to adhere to strict safety standards. In particular, their performance needs to be robust toward volatile clinical inputs. The aim of the work presented in this thesis was to develop a framework for uncertainty handling in medical ML applications as a way to increase their robustness and trustworthiness. In particular, it addresses three root causes for lack of robustness that can be deemed central to the successful clinical translation of ML methods: First, many tasks in medical imaging can be phrased in the language of inverse problems. Most common ML methods aimed at solving such inverse problems implicitly assume that they are well-posed, especially that the problem has a unique solution. However, the solution might be ambiguous. In this thesis, we introduce a data-driven method for analyzing the well-posedness of inverse problems. In addition, we propose a framework to validate the suggested method in a problem-aware manner. Second, simulation is an important tool for the development of medical ML systems due to small in vivo data sets and/or a lack of annotated references (e. g. spatially resolved blood oxygenation (sO 2 )). However, simulation introduces a new uncertainty to the ML pipeline as ML performance guarantees generally rely on the testing data being sufficiently similar to the training data. This thesis addresses the uncertainty by quantifying the domain gap between training and testing data via an out-of-distribution (OoD) detection approach. Third, we introduce a new paradigm for medical ML based on personalized models. In a data-scarce regime with high inter-patient variability, classical ML models cannot be assumed to generalize well to new patients. To overcome this problem, we propose to train ML models on a per-patient basis. This approach circumvents the inter-patient variability, but it requires training without a supervision signal. We address this issue via OoD detection, where the current status quo is encoded as in-distribution (ID) using a personalized ML model. Changes to the status quo are then detected as OoD. While these three facets might seem distinct, the suggested framework provides a unified view of them. The enabling technology is the so-called invertible neural network (INN), which can be used as a flexible and expressive (conditional) density estimator. In this way, they can encode solutions to inverse problems as a probability distribution as well as tackle OoD detection tasks via density-based scores, like the widely applicable information criterion (WAIC). The present work validates our framework on the example of biophotonic imaging. Biophotonic imaging promises the estimation of tissue parameters such as sO 2 in a non-invasive way by evaluating the “fingerprint” of the tissue in the light spectrum. We apply our framework to analyze the well-posedness of the tissue parameter estimation problem at varying spectral and spatial resolutions. We find that with sufficient spectral and/or spatial context, the sO 2 estimation problem is well-posed. Furthermore, we examine the realism of simulated biophotonic data using the proposed OoD approach to gauge the generalization capabilities of our ML models to in vivo data. Our analysis shows a considerable remaining domain gap between the in silico and in vivo spectra. Lastly, we validate the personalized ML approach on the example of non-invasive ischemia monitoring in minimally invasive kidney surgery, for which we developed the first-in-human laparoscopic multispectral imaging system. In our study, we find a strong OoD signal between perfused and ischemic kidney spectra. Furthermore, the proposed approach is video-rate capable. In conclusion, we successfully developed a framework for uncertainty handling in medical ML and validated it using a diverse set of medical ML tasks, highlighting the flexibility and potential impact of our approach. The framework opens the door to robust solutions to applications like (recording) device design, quality control for simulation pipelines, and personalized video-rate tissue parameter monitoring. In this way, this thesis facilitates the development of the next generation of trustworthy ML systems in medicine

Improving the quality of demand forecasts through cross nested logit: a stated choice case study of airport, airline and access mode choice

Airport choice models have been used extensively in recent years to determine the transport planning impacts of large metropolitan areas. However, these studies have typically focussed solely on airports within a given metropolitan area, at a time when passengers are increasingly willing to travel further to access airports. The present paper presents the findings of a study that uses broader, regional data from the East Coast of the United States collected through a stated choice based air travel survey. The study makes use of a Cross- Nested Logit (CNL) structure that allows for the joint representation of inter-alternative correlation along the three choice dimensions of airport, airline and access mode choice. The analysis shows not only significant gains in model fit when moving to this more advanced nesting structure, but the more appropriate cross-elasticity assumptions also lead to more intuitively correct substitution patterns in forecasting examples

Finite-size effects for anisotropic bootstrap percolation: logarithmic corrections

In this note we analyze an anisotropic, two-dimensional bootstrap percolation model introduced by Gravner and Griffeath. We present upper and lower bounds on the finite-size effects. We discuss the similarities with the semi-oriented model introduced by Duarte.Comment: Key words: Bootstrap percolation, anisotropy, finite-size effect

Support services for victims and survivors of child sexual abuse

Some of the content in this report may be distressing to readers.Aims The four broad research aims were to: ● understand more about victims and survivors’ reasons for not accessing support services and any barriers to access; ● learn about victims and survivors’ perceptions and experiences of support services; ● understand what support services victims and survivors think are available to them and how to access them; and ● explore whether there are unmet needs for support services which impact on whether victims and survivors access support. Methods The sample was drawn from 634 adults who self-identified as victims and survivors of child sexual abuse as part of the ‘Abuse during childhood’ module in the Crime Survey for England and Wales (CSEW) year ending March 2019 (Office for National Statistics, 2020).3 A mixed-methods approach was used to explore the above research aims: ● A quantitative online survey4 of 181 victims and survivors from the CSEW recontact sample, including both those who had and had not accessed support. Descriptive and inferential analyses were conducted. ● Twenty-four qualitative in-depth interviews with three groups: (A) eight who had not accessed support services; (B) eight who self-identified as having had positive experiences of support services; and (C) eight who had negative experiences of support services. The interviews were analysed using thematic analysis. These were supplemented with six pen portraits (two from each of the above groups), and a network map to aid understanding of the service landscape. The research participants The ages of the survey respondents ranged from 19 to 74 years, with an average of 47 years. Around four in five identified as female (82%), the majority identified as being of a White ethnic background (92%), and one in three reported having a disability (33%). All regions of England and Wales were represented, with one in four living in London or South East England (26%). Nearly nine in ten identified as heterosexual (89%) Respondents reported experiencing between one and eight types of child sexual abuse. The two most common forms were being kissed or groped on any part of the body in a sexual way (73%) and penetration (64%). The age at first victimisation spanned from infancy to 17 years old, with an average of 9 years old. Child sexual abuse was more likely to have occurred in a familial setting (41%) than an institutional one (11%). Two in five (43%) respondents identified a friend, acquaintance or neighbour as the perpetrator. Around one in four (27%) identified an immediate – typically male – family member as the perpetrator. A stranger was identified by one in five (20%) respondents. Just over one in five respondents had never previously disclosed their experiences of child sexual abuse (21%), while four in five had made a disclosure (79%). Respondents were more than twice as likely to report making a disclosure later in life (75%) than at the time of the abuse (28%). A quarter disclosed at both points (24%)

Semi-domesticated reindeer avoid winter habitats with exotic tree species Pinus contorta

The introduction of exotic tree species can have profound effects on the native environment, including habitat use and movement patterns of animals, as well as becoming a management challenge for other land users. Here, we used GPS data from reindeer (Rangifer tarandus) and remote sensing measurements of lichen cover and soil moisture to assess the effects of the exotic lodgepole pine (Pinus contorta) on reindeer husbandry by the Indig-enous S ' ami in northern Sweden. We used locational data from 67 reindeer for three winters to analyze their habitat selection at the second-order selection (placement of home range in the landscape) and third-order se-lection (selection of sites within the home range) in relation to land cover class, terricolous lichen cover as measure of winter forage abundance, topographic features, and distance to roads. We also analyzed remotely sensed abundance of lichens in different forest types, and the association between these forest types and soil moisture as measure of suitability as lichen habitat. Compared to native P. sylvestris, we found that reindeer avoided stands with P. contorta where trees were higher than three meters. If P. contorta was the dominant tree species, reindeer were 60 % less likely to select these stands compared to stands with P. sylvestris, and 40 % less likely if P. contorta was less dominant at both orders of selection. We also found that reindeer selected areas with higher lichen cover. Lichen cover was lower in P. contorta stands compared to stands of the native P. sylvestris, even though P. contorta occurred mainly on dry soils usually favorable for terricolous lichens. We conclude that planting P. contorta on soils suitable for terricolous lichens is likely to reduce forage availability for reindeer and turn habitats earlier preferred by reindeer into avoided habitat, resulting in an overall reduction of winter grazing grounds. The effects of stands with P. contorta, albeit covering a comparatively small percentage of the reindeer husbandry area, need to be seen in context with generally declining terricolous lichen abundance due to land uses like forestry and other cumulative effects by external pressures on reindeer husbandry

Mid-infrared VIPA Spectrometer for Rapid and Broadband Trace Gas Detection

We present and characterize a 2-D imaging spectrometer based on a virtually-imaged phased array (VIPA) disperser for rapid, high-resolution molecular detection using mid-infrared (MIR) frequency combs at 3.1 and 3.8 \mu m. We demonstrate detection of CH4 at 3.1 \mu m with >3750 resolution elements spanning >80 nm with ~600 MHz resolution in a <10 \mu s acquisition time. In addition to broadband detection, rapid, time-resolved single-image detection is demonstrated by capturing dynamic concentration changes of CH4 at a rate of ~375 frames per second. Changes in absorption above the noise floor of 5\times 10-4 are readily detected on the millisecond time scale, leading to important future applications such as real time monitoring of trace gas concentrations and detection of reactive intermediates