Opportunistic Hip Fracture Risk Prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study

Schmarje, Lars; Reinhold, Stefan; Damm, Timo; Orwoll, Eric; Glüer, Claus‐C.; Koch, Reinhard

doi:10.1007/978-3-031-16919-9_10

Cited by 2 publications

(2 citation statements)

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“…This dissertation is divided into three main parts, except for the introduction and conclusion, as shown in Figure 1.2. This is a cumulative work of my related scientific research, so the details of the individual papers [37,63,147,148,149,150,151,152,153,154,155,156,185] are listed in Section 1.5 and reprinted in Appendix A. The focus of this dissertation is to highlight the connections and conclusions between my individual papers based on the research questions defined above.…”

Section: Structure Of Dissertationmentioning

confidence: 99%

“…Often humans are used to provide the label on the same modality that is shown to the neural network during training. Only in rare cases is an external GT available, such as the fracture of a bone in a retro-perspective [153] or from another domain [146]. Thus, the common case is that humans provide the annotations to generate the label.…”

Section: Quantity and Quality Of Datamentioning

confidence: 99%

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Addressing the Challenge of Ambiguous Data in Deep Learning

Schmarje

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In machine learning, the availability of high-quality labeled data is essential for training accurate models. However, humans often disagree among themselves or over time when labeling or annotating image classification data. As a result, they create ambiguous data that poses a significant challenge to deep learning. This research focuses on addressing this issue by proposing an overview that defines the problem, provides the necessary data for research, establishes evaluation metrics and presents methods for solving the problem. The ultimate goal is to develop an effective annotation strategy for image classification based on several thousand different experiments to improve data quality while reducing cost. The main idea of this dissertation is not to rely on one annotation per image, but to consider the average of multiple annotations. However, this would not be feasible in reality without reducing the cost of acquiring such annotations. This dissertation uses proposals as a guide during the annotation process to improve the consistency of the annotations and to reduce the annotation time and thus the associated costs. The use of proposals could introduce a bias into the data. Analysis of this bias and its introduction led to possible methods to minimize or reverse it. All this research is finally unified in an annotation strategy in the form of a flowchart that allows future research to easily understand the necessary steps needed to produce high quality data for their specific use case with as little effort as possible. This strategy is successfully verified on a real biomedical task. In conclusion, this thesis presents a comprehensive investigation of the challenges posed by annotating ambiguous data in deep learning. This research provides an effective strategy to address the fundamental issue of data quality, thereby improving the quality and reliability of models and advancing the field of deep learning as a whole.

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