Unsupervised pattern recognition methods for exploratory analysis of industrial process data

The rapid growth of data storage capacities of process automation systems provides new possibilities to analyze behavior of industrial processes. As existence of large volumes of measurement data is a rather new issue in the process industry, long tradition of using data analysis techniques in that field does not yet exist. In this thesis, unsupervised pattern recognition methods are shown to represent one potential and computationally efficient approach in exploratory analysis of such data. This thesis consists of an introduction and six publications. The introduction contains a survey on process monitoring and data analysis methods, exposing the research which has been carried out in the fields so far. The introduction also points out the tasks in the process management framework where the methods considered in this thesis - self-organizing maps and cluster analysis - can be benefited. The main contribution of this thesis consists of two parts. The first one is the use of the existing and development of novel SOM-based methods for process monitoring and exploratory data analysis purposes. The second contribution is a concept where cluster analysis is used to extract and identify operational states of a process from measured data. In both cases the methods have been applied in exploratory analysis of real data from processes in the wood processing industry.reviewe

Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation

Federated Learning (FL) is a distributed machine learning approach that safeguards privacy by creating an impartial global model while respecting the privacy of individual client data. However, the conventional FL method can introduce security risks when dealing with diverse client data, potentially compromising privacy and data integrity. To address these challenges, we present a differential privacy (DP) federated deep learning framework in medical image segmentation. In this paper, we extend our similarity weight aggregation (SimAgg) method to DP-SimAgg algorithm, a differentially private similarity-weighted aggregation algorithm for brain tumor segmentation in multi-modal magnetic resonance imaging (MRI). Our DP-SimAgg method not only enhances model segmentation capabilities but also provides an additional layer of privacy preservation. Extensive benchmarking and evaluation of our framework, with computational performance as a key consideration, demonstrate that DP-SimAgg enables accurate and robust brain tumor segmentation while minimizing communication costs during model training. This advancement is crucial for preserving the privacy of medical image data and safeguarding sensitive information. In conclusion, adding a differential privacy layer in the global weight aggregation phase of the federated brain tumor segmentation provides a promising solution to privacy concerns without compromising segmentation model efficacy. By leveraging DP, we ensure the protection of client data against adversarial attacks and malicious participants

Additive Benefits of Radium-223 Dichloride and Bortezomib Combination in a Systemic Multiple Myeloma Mouse Model

Osteolytic bone disease is a hallmark of multiple myeloma (MM) mediated by MM cell proliferation, increased osteoclast activity, and suppressed osteoblast function. The proteasome inhibitor bortezomib targets MM cells and improves bone health in MM patients. Radium-223 dichloride (radium-223), the first targeted alpha therapy approved, specifically targets bone metastases, where it disrupts the activity of both tumor cells and tumor-supporting bone cells in mouse models of breast and prostate cancer bone metastasis. We hypothesized that radium-223 and bortezomib combination treatment would have additive effects on MM. In vitro experiments revealed that the combination treatment inhibited MM cell proliferation and demonstrated additive efficacy. In the systemic, syngeneic 5TGM1 mouse MM model, both bortezomib and radium-223 decreased the osteolytic lesion area, and their combination was more effective than either monotherapy alone. Bortezomib decreased the number of osteoclasts at the tumor-bone interface, and the combination therapy resulted in almost complete eradication of osteoclasts. Furthermore, the combination therapy improved the incorporation of radium-223 into MM-bearing bone. Importantly, the combination therapy decreased tumor burden and restored body weights in MM mice. These results suggest that the combination of radium-223 with bortezomib could constitute a novel, effective therapy for MM and, in particular, myeloma bone disease