Deep learning approaches have been explored for surgical tool classification in laparoscopic videos. Convolutional neural networks (CNN) are prominent among the proposed approaches. However, concerns about the robustness and generalisability of CNN approaches have been raised. This paper evaluates CNN generalisability across different procedures and in data from different surgical settings. Moreover, generalisation performance to new types of procedures is assessed and insights are provided into the effect of increasing the size and representativeness of training data on the generalisation capabilities of CNN. Five experiments were conducted using three datasets. The DenseNet-121 model showed high generalisation capability within the dataset, with a mean average precision of 93%. However, the model performance diminished on data from different surgical sites and across procedure types (27% and 38%, respectively). The generalisation performance of the CNN model was improved by increasing the quantity of training videos on data of the same procedure type (the best improvement was 27%). These results highlight the importance of evaluating the performance of CNN models on data from unseen sources in order to determine their real classification capabilities. While the analysed CNN model yielded reasonably robust performance on data from different subjects, it showed a moderate reduction in performance for different surgical settings.

The estimation of the 3D position of an object is one of the most important topics in the computer vision field. Where the final aim is to create automated solutions that can localize and detect objects from images, new high-performing models and algorithms are needed. Due to lack of relevant information in the single 2D images, approximating the 3D position can be considered a complex problem. This thesis describes a method based on two deep learning models: the image net and the temporal net that can tackle this task. The former is a deep convolutional neural network with the intention to extract meaningful features from the images, while the latter exploits the temporal information to reach a more robust prediction. This solution reaches a better Mean Absolute Error compared to already existing computer vision methods on different conditions and configurations. A new data-driven pipeline has been created to deal with 2D videos and extract the 3D information of an object. The same architecture can be generalized to different domains and applications. / Uppskattning av 3D-positionen för ett objekt är ett viktigt område inom datorseende. Då det slutliga målet är att skapa automatiserade lösningar som kan lokalisera och upptäcka objekt i bilder, behövs nya, högpresterande modeller och algoritmer. Bristen på relevant information i de enskilda 2D-bilderna gör att approximering av 3D-positionen blir ett komplext problem. Denna uppsats beskriver en metod baserad på två djupinlärningsmodeller: image net och temporal net. Den förra är ett djupt nätverk som kan extrahera meningsfulla egenskaper från bilderna, medan den senare utnyttjar den tidsmässiga informationen för att kunna göra mer robusta förutsägelser. Denna lösning erhåller ett lägre genomsnittligt absolut fel jämfört med existerande metoder, under olika villkor och konfigurationer. En ny datadriven arkitektur har skapats för att hantera 2D-videoklipp och extrahera 3D-informationen för ett objekt. Samma arkitektur kan generaliseras till olika domäner och applikationer.

要旨ファイルを差し替え（2023-01-23） / 京都大学 / 新制・課程博士 / 博士(薬学) / 甲第23144号 / 薬博第844号 / 新制||薬||242(附属図書館) / 京都大学大学院薬学研究科薬学専攻 / (主査)教授 高須 清誠, 教授 石濱 泰, 教授 大野 浩章 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

京都大学 / 新制・論文博士 / 博士(医学) / 乙第13501号 / 論医博第2260号 / 新制||医||1061(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 小林 恭, 教授 中本 裕士, 教授 黒田 知宏 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

In shotgun proteomics, the liquid chromatography step is used to separate peptides in order to analyze as few as possible at the same time in the mass spectrometry step. Each peptide has a retention time, that is how long it takes to pass through the chromatography column. Prediction of the retention time can be used to gain increased identification of peptides or in order to create targeted proteomics experiments. Using machine learning methods such as support vector machines has given a high prediction accuracy, but such methods require known features that the retention time depends on. In this thesis we let a convolutional network, learn to rank the retention times instead of predicting the retention times themselves. We also tested how the prediction accuracy depends on the size of the training set. We found that pairwise ranking of peptides outperforms pointwise ranking and that adding more training data increased accuracy until the end without an increase in training time. This implies that accuracy can be further increased by training on even greater training sets.

With increasing data and technology, multiple types of data from the same set of nodes have been generated. Since each data modality contains a unique aspect of the underlying mechanisms, multiple datatypes are integrated. In addition to multiple datatypes, networks are important to store information representing associations between entities such as genes of a protein-protein interaction network and authors of a citation network. Recently, some advanced approaches to graph-structured data leverage node associations and features simultaneously, called Graph Neural Network (GNN), but they have limitations for integrative approaches. The overall aim of this dissertation is to integrate multiple data modalities on graph-structured data to infer some context-specific gene regulation and predict outcomes of interest. To this end, first, we introduce a computational tool named CRINET to infer genome-wide competing endogenous RNA (ceRNA) networks. By integrating multiple data properly, we had a better understanding of gene regulatory circuitry addressing important drawbacks pertaining to ceRNA regulation. We tested CRINET on breast cancer data and found that ceRNA interactions and groups were significantly enriched in the cancer-related genes and processes. CRINET-inferred ceRNA groups supported the studies claiming the relation between immunotherapy and cancer. Second, we present SUPREME, a node classification framework, by comprehensively analyzing multiple data and associations between nodes with graph convolutions on multiple networks. Our results on survival analysis suggested that SUPREME could demystify the characteristics of classes with proper utilization of multiple data and networks. Finally, we introduce an attention-aware fusion approach, called GRAF, which fuses multiple networks and utilizes attention mechanisms on graph-structured data. Utilization of learned node- and association-level attention with network fusion allowed us to prioritize the edges properly, leading to improvement in the prediction results. Given the findings of all three tools and their outperformance over state-of-the-art methods, the proposed dissertation shows the importance of integrating multiple types of data and the exploitation of multiple graph structured data.

Indiana University-Purdue University Indianapolis (IUPUI) / Biomechanical Big Data is of great significance to precision health applications, among which we take special interest in Physical Activity Detection (PAD). In this study, we have performed extensive research on deep learning-based PAD from biomechanical big data, focusing on the challenges raised by the need for real-time edge inference. First, considering there are many places we can place the motion sensors, we have thoroughly compared and analyzed the location difference in terms of deep learning-based PAD performance. We have further compared the difference among six sensor channels (3-axis accelerometer and 3-axis gyroscope). Second, we have selected the optimal sensor and the optimal sensor channel, which can not only provide sensor usage suggestions but also enable ultra-lowpower application on the edge. Third, we have investigated innovative methods to minimize the training effort of the deep learning model, leveraging the transfer learning strategy. More specifically, we propose to pre-train a transferable deep learning model using the data from other subjects and then fine-tune the model using limited data from the target-user. In such a way, we have found that, for single-channel case, the transfer learning can effectively increase the deep model performance even when the fine-tuning effort is very small. This research, demonstrated by comprehensive experimental evaluation, has shown the potential of ultra-low-power PAD with minimized sensor stream, and minimized training effort. / 2023-06-01

Indiana University-Purdue University Indianapolis (IUPUI) / Semantic segmentation is a machine learning task that is seeing increased utilization in multiples fields, from medical imagery, to land demarcation, and autonomous vehicles. Semantic segmentation performs the pixel-wise classification of images, creating a new, seg- mented representation of the input that can be useful for detected various terrain and objects within and image. Recently, convolutional neural networks have been heavily utilized when creating neural networks tackling the semantic segmentation task. This is particularly true in the field of autonomous driving systems. The requirements of automated driver assistance systems (ADAS) drive semantic seg- mentation models targeted for deployment on ADAS to be lightweight while maintaining accuracy. A commonly used method to increase accuracy in the autonomous vehicle field is to fuse multiple sensory modalities. This research focuses on leveraging the fusion of long wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent perfor- mance gaps when using visual imagery alone. This comes with a host of benefits, such as increase performance in various lighting conditions and adverse environmental conditions. Utilizing this fusion technique is an effective method of increasing the accuracy of a semantic segmentation model. Being a lightweight architecture is key for successful deployment on ADAS, as these systems often have resource constraints and need to operate in real-time. Multi-Spectral Fusion Network (MFNet) [1] accomplishes these parameters by leveraging a sensory fusion approach, and as such was selected as the baseline architecture for this research. Many improvements were made upon the baseline architecture by leveraging a variety of techniques. Such improvements include the proposal of a novel loss function categori- cal cross-entropy dice loss, introduction of squeeze and excitation (SE) blocks, addition of pyramid pooling, a new fusion technique, and drop input data augmentation. These improve- ments culminated in the creation of the Fast Thermal Fusion Network (FTFNet). Further improvements were made by introducing depthwise separable convolutional layers leading to lightweight FTFNet variants, FTFNet Lite 1 & 2. 13 The FTFNet family was trained on the Multi-Spectral Road Scenarios (MSRS) and MIL- Coaxials visual/LWIR datasets. The proposed modifications lead to an improvement over the baseline in mean intersection over union (mIoU) of 2.92% and 2.03% for FTFNet and FTFNet Lite 2 respectively when trained on the MSRS dataset. Additionally, when trained on the MIL-Coaxials dataset, the FTFNet family showed improvements in mIoU of 8.69%, 4.4%, and 5.0% for FTFNet, FTFNet Lite 1, and FTFNet Lite 2.

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The deployment of distributed photovoltaic (PV) is accelerating worldwide. Understanding when and where PV systems will generate electricity is valuable as it affects the power balance in the grids. One way of obtaining this information is simulating the PV power production of systems detected in Remotely Sensed Data (RSD). The use of aerial imagery and machine learning models has proven effective for identifying solar energy facilities. In a Swedish research project, a Convolutional Neural Network (CNN) could identify 95% of all PV systems within a municipality. Furthermore, using Light Detection and Ranging (LiDAR) data, the orientation and area of detected PV systems can be estimated. Combining this information, with local weather and irradiance data, the historic PV power generation can be simulated. The purpose of this study is to adapt and validate a model for simulating historic decentralized PV electricity generation, based on an optimization tool developed by Becquerel Sweden, and further develop the model to simulate aggregated electricity generation on a municipality level where the individual orientation of each PV system is taken into account. The model has a temporal resolution of 1 hour and a spatial resolution of 2.5×2.5 km.  A regression analysis demonstrated that the simulated generation corresponds well to the measured generation of 7 reference systems, with coefficients of determination ranging from 0.69–0.84. However, the model tends to overestimate the production compared to the measured values, with a higher total simulated production and positive mean bias errors. The correlation of the measured and generated PV power was similar, when simulating using orientations provided by the reference facility owners and LiDAR approximated orientations. Generic module parameters and an average DC/AC ratio were derived in this study, enabling simulation on a municipal level. Due to available RSD, Knivsta Municipality was the object for this study. The aggregated PV electricity generation was simulated for 2022, using both an estimation of optimal conditions and an estimation of real conditions. This was compared to the assumption that all installed AC capacity in the municipality is fed to the grid. The results show that during the highest production hour, the electricity generation resulting from estimated optimal conditions, exceeds the total installed AC capacity, while the simulation using approximated real conditions never reach the total installed AC capacity. However, the average hourly production for both scenarios, never exceeds 45% of the total installed AC capacity.