Visual Question Answering in the Medical Domain

Medical visual question answering (Med-VQA) is a machine learning task that aims to create a system that can answer natural language questions based on given medical images. Although there has been rapid progress on the general VQA task, less progress has been made on Med-VQA due to the lack of large-scale annotated datasets. In this paper, we present domain-specific pre-training strategies, including a novel contrastive learning pretraining method, to mitigate the problem of small datasets for the Med-VQA task. We find that the model benefits from components that use fewer parameters. We also evaluate and discuss the model's visual reasoning using evidence verification techniques. Our proposed model obtained an accuracy of 60% on the VQA-Med 2019 test set, giving comparable results to other state-of-the-art Med-VQA models.Comment: 8 pages, 7 figures, Accepted to DICTA 2023 Conferenc

Analyzing an Embedded Sensor with Timed Automata in Uppaal

International audienceAn infrared sensor is modeled and analyzed in Uppaal. The sensor typifies the sort of component that engineers regularly integrate into larger systems by writing interface hardware and software. In all, three main models are developed. For the first, the timing diagram of the sensor is interpreted and modeled as a timed safety automaton. This model serves as a specification for the complete system. A second model that emphasizes the separate roles of driver and sensor is then developed. It is validated against the timing diagram model using an existing construction that permits the verification of timed trace inclusion, for certain models, by reachability analysis (i.e., model checking). A transmission correctness property is also stated by means of an auxiliary automaton and shown to be satisfied by the model. A third model is created from an assembly language driver program, using a direct translation from the instruction set of a processor with simple timing behavior. This model is validated against the driver component of the second timing diagram model using the timed trace inclusion validation technique. While no pretense is made of providing a general means to verify systems, The approach and its limitations offer insight into the nature and challenges of programming in real time

Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning

Purpose: Ultrasound is the most commonly used medical imaging modality for diagnosis and screening in clinical practice. Due to its safety profile, noninvasive nature and portability, ultrasound is the primary imaging modality for fetal assessment in pregnancy. Current ultrasound processing methods are either manual or semi-automatic and are therefore laborious, time-consuming and prone to errors, and automation would go a long way in addressing these challenges. Automated identification of placental changes at earlier gestation could facilitate potential therapies for conditions such as fetal growth restriction and pre-eclampsia that are currently detected only at late gestational age, potentially preventing perinatal morbidity and mortality. Methods: We propose an automatic three-dimensional multi-modal (B-mode and power Doppler) ultrasound segmentation of the human placenta using deep learning combined with different fusion strategies.We collected data containing Bmode and power Doppler ultrasound scans for 400 studies. Results: We evaluated different fusion strategies and state-of-the-art image segmentation networks for placenta segmentation based on standard overlap- and boundary-based metrics. We found that multimodal information in the form of B-mode and power Doppler scans outperform any single modality. Furthermore, we found that B-mode and power Doppler input scans fused at the data level provide the best results with a mean Dice Similarity Coefficient (DSC) of 0.849. Conclusion: We conclude that the multi-modal approach of combining B-mode and power Doppler scans is effective in segmenting the placenta from 3D ultrasound scans in a fully automated manner and is robust to quality variation of the datasets

Political Competition and the Initiation of International Conflict: A New Perspective on the Institutional Foundations of Democratic Peace

Although the empirical pattern of democratic peace is well-established, debate continues over its theoretical explanation. While theory tends to focus on specific institutional or normative characteristics within regimes, empirical studies often test this indirectly, using aggregate measures of types of political regimes as a whole. The analysis in this paper more directly assesses expectations about core characteristics of regime type for the likelihood of interstate conflict initiation. We advance a theory about political competition which leads to expectations that it, rather than political participation or constraining institutions, is the most important source of the observed democratic peace. Specifically, leaders facing a viable opposition are most concerned with forestalling potential criticism of their foreign policies. Initiating conflict with a democracy would leave them vulnerable to opposition criticism on normative and costs-of-war bases. Potential vulnerability to such opposition criticism can be seen as a necessary condition for the operation of mechanisms such as audience costs or public-goods logic proposed by existing theories. We present robust statistical and machine-learning based results for directed dyads in the post-World War II era supporting our argument that high-competition states avoid initiating fights with democracies.Benjamin Goldsmith gratefully acknowledges support from the Australian Research Council through a Future Fellowship (FT140100763)

Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images

Segmentation of the sigmoid colon is a crucial aspect of treating diverticulitis. It enables accurate identification and localisation of inflammation, which in turn helps healthcare professionals make informed decisions about the most appropriate treatment options. This research presents a novel deep learning architecture for segmenting the sigmoid colon from Computed Tomography (CT) images using a modified 3D U-Net architecture. Several variations of the 3D U-Net model with modified hyper-parameters were examined in this study. Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation (csSE) were also used to improve the model performance. The networks were trained using manually annotated sigmoid colon. A five-fold cross-validation procedure was used on a test dataset to evaluate the network's performance. As indicated by the maximum Dice similarity coefficient (DSC) of 56.92+/-1.42%, the application of PyP and csSE techniques improves segmentation precision. We explored ensemble methods including averaging, weighted averaging, majority voting, and max ensemble. The results show that average and majority voting approaches with a threshold value of 0.5 and consistent weight distribution among the top three models produced comparable and optimal results with DSC of 88.11+/-3.52%. The results indicate that the application of a modified 3D U-Net architecture is effective for segmenting the sigmoid colon in Computed Tomography (CT) images. In addition, the study highlights the potential benefits of integrating ensemble methods to improve segmentation precision.Comment: 8 Pages, 6 figures, Accepted at IEEE DICTA 202

Automated analysis of internal quantum efficiency using chain order regression

Spectral analysis of internal quantum efficiency (IQE) measurements of solar cells is a powerful method to identify performance-limiting mechanisms in photovoltaic devices. This analysis is usually performed using complex curve-fitting methods to extract various electrical and optical performance parameters. As these traditional fitting methods are not easy to use and are often sensitive to measurement noise, many users do not utilize the full potential of the IQE measurements to provide the key properties of their solar cells. In this study, we propose a simplified approach to analyze IQE curves of silicon solar cells using machine learning models that are trained to extract valuable information regarding the cell's performance and decoupling the parasitic absorption of the anti-reflection coating. The proposed approach is demonstrated to be a powerful characterization tool for solar cells as machine learning unlocks the full potential of IQE measurements