Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

Multimodal imaging to study the morphochemistry of basal cell carcinoma

Basal cell carcinoma is the most abundant malignant neoplasm in humans, the pathology of which is characterized by an abnormal proliferation of basal cells. Basal cell carcinoma can show a variety of different morphologies, which are based on different cellular biology. Furthermore, the carcinoma often grows invisibly to the eye imbedded in the surrounding skin. Therefore, in some cases its clinical detection is challenging. Thus, our work aims at establishing an unsupervised tissue classification method based on multimodal imaging and the application of chemometrics to discriminate basal cell carcinoma from non-diseased tissue. A case study applying multimodal imaging to ex-vivo sections of basal cell carcinoma is presented. In doing so, we apply a combination of various linear and non-linear imaging modalities, i.e. fluorescence, Raman and second-harmonic generation microscopy, to study the morphochemistry of basal cell carcinoma. The joint information content obtained by such multimodal approach in studying various aspects of the malignant tissue alterations associated with basal cell carcinoma is discussed. [GRAPHICS] Multimodal imaging combining coherent anti-Stokes Raman scattering, second-harmonic generation and two-photo fluorescence is combined with Raman spectroscopy to investigate the morphochemistry of human basal cell carcinoma

Impact of COVID-19-adapted guidelines using different airway management strategies on resuscitation quality in out-of-hospital-cardiac-arrest – a randomised manikin study

Abstract Background Although airway management for paramedics has moved away from endotracheal intubation towards extraglottic airway devices in recent years, in the context of COVID-19, endotracheal intubation has seen a revival. Endotracheal intubation has been recommended again under the assumption that it provides better protection against aerosol liberation and infection risk for care providers than extraglottic airway devices accepting an increase in no-flow time and possibly worsen patient outcomes. Methods In this manikin study paramedics performed advanced cardiac life support with non-shockable (Non-VF) and shockable rhythms (VF) in four settings: ERC guidelines 2021 (control), COVID-19-guidelines using videolaryngoscopic intubation (COVID-19-intubation), laryngeal mask (COVID-19-Laryngeal-Mask) or a modified laryngeal mask modified with a shower cap (COVID-19-showercap) to reduce aerosol liberation simulated by a fog machine. Primary endpoint was no-flow-time, secondary endpoints included data on airway management as well as the participants‘ subjective assessment of aerosol release using a Likert-scale (0 = no release–10 = maximum release) were collected and statistically compared. Continuous Data was presented as mean ± standard deviation. Interval-scaled Data were presented as median and Q1 and Q3. Results A total of 120 resuscitation scenarios were completed. Compared to control (Non-VF:11 ± 3 s, VF:12 ± 3 s) application of COVID-19-adapted guidelines lead to prolonged no-flow times in all groups (COVID-19-Intubation: Non-VF:17 ± 11 s, VF:19 ± 5 s;p ≤ 0.001; COVID-19-laryngeal-mask: VF:15 ± 5 s,p ≤ 0.01; COVID-19-showercap: VF:15 ± 3 s,p ≤ 0.01). Compared to COVID-19-Intubation, the use of the laryngeal mask and its modification with a showercap both led to a reduction of no-flow-time(COVID-19-laryngeal-mask: Non-VF:p = 0.002;VF:p ≤ 0.001; COVID-19-Showercap: Non-VF:p ≤ 0.001;VF:p = 0.002) due to a reduced duration of intubation (COVID-19-Intubation: Non-VF:40 ± 19 s;VF:33 ± 17 s; both p ≤ 0.01 vs. control, COVID-19-Laryngeal-Mask (Non-VF:15 ± 7 s;VF:13 ± 5 s;p > 0.05) and COVID-19-Shower-cap (Non-VF:15 ± 5 s;VF:17 ± 5 s;p > 0.05). The participants rated aerosol liberation lowest in COVID-19-intubation (median:0;Q1:0,Q3:2;p < 0.001vs.COVID-19-laryngeal-mask and COVID-19-showercap) compared to COVID-19-shower-cap (median:3;Q1:1,Q3:3 p < 0.001vs.COVID-19-laryngeal-mask) or COVID-19-laryngeal-mask (median:9;Q1:6,Q3:8). Conclusions COVID-19-adapted guidelines using videolaryngoscopic intubation lead to a prolongation of no-flow time. The use of a modified laryngeal mask with a shower cap seems to be a suitable compromise combining minimal impact on no-flowtime and reduced aerosol exposure for the involved providers

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Comment: 93 page