Visual Representation Learning with Minimal Supervision

Computer vision intends to provide the human abilities of understanding and interpreting the visual surroundings to computers. An essential element to comprehend the environment is to extract relevant information from complex visual data so that the desired task can be solved. For instance, to distinguish cats from dogs the feature 'body shape' is more relevant than 'eye color' or the 'amount of legs'. In traditional computer vision it is conventional to develop handcrafted functions that extract specific low-level features such as edges from visual data. However, in order to solve a particular task satisfactorily we require a combination of several features. Thus, the approach of traditional computer vision has the disadvantage that whenever a new task is addressed, a developer needs to manually specify all the features the computer should look for. For that reason, recent works have primarily focused on developing new algorithms that teach the computer to autonomously detect relevant and task-specific features. Deep learning has been particularly successful for that matter. In deep learning, artificial neural networks automatically learn to extract informative features directly from visual data. The majority of developed deep learning strategies require a dataset with annotations which indicate the solution of the desired task. The main bottleneck is that creating such a dataset is very tedious and time-intensive considering that every sample needs to be annotated manually. This thesis presents new techniques that attempt to keep the amount of human supervision to a minimum while still reaching satisfactory performances on various visual understanding tasks. In particular, this thesis focuses on self-supervised learning algorithms that train a neural network on a surrogate task where no human supervision is required. We create an artificial supervisory signal by breaking the order of visual patterns and asking the network to recover the original structure. Besides demonstrating the abilities of our model on common computer vision tasks such as action recognition, we additionally apply our model to biomedical scenarios. Many research projects in medicine involve profuse manual processes that extend the duration of developing successful treatments. Taking the example of analyzing the motor function of neurologically impaired patients we show that our self-supervised method can help to automate tedious, visually based processes in medical research. In order to perform a detailed analysis of motor behavior and, thus, provide a suitable treatment, it is important to discover and identify the negatively affected movements. Therefore, we propose a magnification tool that can detect and enhance subtle changes in motor function including motor behavior differences across individuals. In this way, our automatic diagnostic system does not only analyze apparent behavior but also facilitates the perception and discovery of impaired movements. Learning a feature representation without requiring annotations significantly reduces human supervision. However, using annotated dataset leads generally to better performances in contrast to self-supervised learning methods. Hence, we additionally examine semi-supervised approaches which efficiently combine few annotated samples with large unlabeled datasets. Consequently, semi-supervised learning represents a good trade-off between annotation time and accuracy

Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats

The majority of stroke patients develop post-stroke fatigue, a symptom which impairs motivation and diminishes the success of rehabilitative interventions. We show that large cortical strokes acutely reduce activity levels in rats for 1-2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1-2 weeks) or late (3-4 weeks after stroke) to an individually monitored enriched environment to stimulate self-controlled high-intensity sensorimotor training. A group of animals received Anti-Nogo antibodies for the first two weeks after stroke, a neuronal growth promoting immunotherapy already in clinical trials. Early exposure to the enriched environment resulted in poor outcome: Training intensity was correlated to enhanced systemic inflammation and functional impairment. In contrast, animals starting intense sensorimotor training two weeks after stroke preceded by the immunotherapy revealed better recovery with functional outcome positively correlated to the training intensity and the extent of re-innervation of the stroke denervated cervical hemi-cord. Our results suggest stroke-induced fatigue as a biological purposeful reaction of the organism during neuronal remodeling, enabling new circuit formation which will then be stabilized or pruned in the subsequent rehabilitative training phase. However, intense training too early may lead to wrong connections and is thus less effective

Data_Sheet_1_Perioperative prothrombin complex concentrate and fibrinogen administration are associated with thrombotic complications after liver transplant.docx

BackgroundUse of intraoperative prothrombin complex concentrates (PCC) and fibrinogen concentrate administration has been linked to thrombotic events. However, it is unknown if its use is associated with thrombotic events after liver transplant.Methods and analysisWe conducted a post hoc analysis of a prospectively conducted registry database study on patients who underwent liver transplant between 2004 and 2017 at Heidelberg University Hospital, Heidelberg, Germany. Univariate and multivariate analyses were used to determine the association between PCC and fibrinogen concentrate administration and thrombotic complications.ResultsData from 939 transplantations were included in the analysis. Perioperative PCC or fibrinogen administration was independently associated with the primary composite endpoint Hepatic artery thrombosis (HAT), Portal vein thrombosis (PVT), and inferior vena cava thrombosis [adjusted HR: 2.018 (1.174; 3.468), p = 0.011]. PCC or fibrinogen administration was associated with the secondary endpoints 30-day mortality (OR 4.225, p ConclusionA critical review of established strategies in coagulation management during liver transplantation is warranted. Perioperative caregivers should exercise caution when administering coagulation factor concentrate during liver transplant surgery. Prospective randomized controlled trials are needed to establish causality for the relationship between coagulation factors and thrombotic events in liver transplantation. Further studies should be tailored to identify patient subgroups that will likely benefit from PCC or fibrinogen administration.</p