16 research outputs found
Nutzung von Routinedaten in einer Elektronischen Patientenakte für eine Evaluationsstudie: Datenmanagement und Datenschutzkonzept in OPTIMAL@NRW
Semantic modeling of cell damage prediction: a machine learning approach at human-level performance in dermatology
Abstract Machine learning is transforming the field of histopathology. Especially in classification related tasks, there have been many successful applications of deep learning already. Yet, in tasks that rely on regression and many niche applications, the domain lacks cohesive procedures that are adapted to the learning processes of neural networks. In this work, we investigate cell damage in whole slide images of the epidermis. A common way for pathologists to annotate a score, characterizing the degree of damage for these samples, is the ratio between healthy and unhealthy nuclei. The annotation procedure of these scores, however, is expensive and prone to be noisy among pathologists. We propose a new measure of damage, that is the total area of damage, relative to the total area of the epidermis. In this work, we present results of regression and segmentation models, predicting both scores on a curated and public dataset. We have acquired the dataset in collaborative efforts with medical professionals. Our study resulted in a comprehensive evaluation of the proposed damage metrics in the epidermis, with recommendations, emphasizing practical relevance for real world applications
Semi-supervised Learning From Demonstration through Program Synthesis: An Inspection Robot Case Study
Semi-supervised learning improves the performance of supervised machine
learning by leveraging methods from unsupervised learning to extract
information not explicitly available in the labels. Through the design of a
system that enables a robot to learn inspection strategies from a human
operator, we present a hybrid semi-supervised system capable of learning
interpretable and verifiable models from demonstrations. The system induces a
controller program by learning from immersive demonstrations using sequential
importance sampling. These visual servo controllers are parametrised by
proportional gains and are visually verifiable through observation of the
position of the robot in the environment. Clustering and effective particle
size filtering allows the system to discover goals in the state space. These
goals are used to label the original demonstration for end-to-end learning of
behavioural models. The behavioural models are used for autonomous model
predictive control and scrutinised for explanations. We implement causal
sensitivity analysis to identify salient objects and generate counterfactual
conditional explanations. These features enable decision making interpretation
and post hoc discovery of the causes of a failure. The proposed system expands
on previous approaches to program synthesis by incorporating repellers in the
attribution prior of the sampling process. We successfully learn the hybrid
system from an inspection scenario where an unmanned ground vehicle has to
inspect, in a specific order, different areas of the environment. The system
induces an interpretable computer program of the demonstration that can be
synthesised to produce novel inspection behaviours. Importantly, the robot
successfully runs the synthesised program on an unseen configuration of the
environment while presenting explanations of its autonomous behaviour.Comment: In Proceedings AREA 2020, arXiv:2007.1126