Automatic Report Generation for Histopathology images using pre-trained Vision Transformers

Deep learning for histopathology has been successfully used for disease classification, image segmentation and more. However, combining image and text modalities using current state-of-the-art methods has been a challenge due to the high resolution of histopathology images. Automatic report generation for histopathology images is one such challenge. In this work, we show that using an existing pre-trained Vision Transformer in a two-step process of first using it to encode 4096x4096 sized patches of the Whole Slide Image (WSI) and then using it as the encoder and an LSTM decoder for report generation, we can build a fairly performant and portable report generation mechanism that takes into account the whole of the high resolution image, instead of just the patches. We are also able to use representations from an existing powerful pre-trained hierarchical vision transformer and show its usefulness in not just zero shot classification but also for report generation.Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 09 page

Designing of a Wind Power System in Azara area of Guwahati, Assam

Wind and Solar energy are the non-conventional sources of energy. It is predicted that the conventional sources of energy will get exhausted in the mere future. Taking that into consideration, a portable model is developed for the utilization of wind energy. Energy produced from the movement of blades depends on different parameters like curvature of blade, blade angle and also the associated drive system. Apart from that it is also dependent on the external factors like wind coming from nearby water sources, texture (i.e. presence of trees, mountains, sand fields) which behaves as wind corridors, seasons etc. The model can produce an output of 12W-16Wdepending on the wind speed. The paper is stressed on the various types of blade design suitable for operating in low wind velocities in Azara area whose exact location is 19.08130 0N and 72.888600E (location of School of Technology, Assam Don Bosco University)

A High Voltage Gain Boost Converter: Concept of DC Power Transfer Using Mutual Inductors

A high voltage boost converter is being prototyped in an artificial software-based environment of MATLAB/ SIMULINK and identifies the practical conditions of the converter. A direct current (DC) input voltage is being boosted to a higher magnitude by multiplying a gain factor in a dynamic process of DC power transfer by cascading three mutual inductors in a single core. Input voltage is being switched by primary IGBT switches creating simultaneous charging and discharging of primary inductor, hence induces identical voltage in two secondary inductors. Inductors are charged and power is transferred to a parallel capacitor and finally to the resistive load in accurate control of duty cycles

High Voltage Boost Converters: A Review on Different Methodologies and Topologies

Power converters are a fundamental element in the industries, micro-grids and households appliances providing all the necessary power regulation increasing the flexibility of the voltage, current, power, and phase. In this review a number of boost converters are studied, responsible for converting a low direct current voltage to a higher magnitude using a number of different methods including coupled inductors, series combination of a capacitor and two parallel inductors and an inductor discharging to two series connected capacitors in a transfer of power. The converters encounter two major practical issues sudden rise in di/dt and dv/dt that drastically reduces the efficiency and increases power loss in passive elements and stress in active switches

Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48\%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Artificial intelligence-based analytics for diagnosis of small bowel enteropathies and black box feature detection

Objectives: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies.Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs) including one with multi-zoom architecture. Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the models\u27 decision making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively.Results: 461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37·5 (19·0 to 121·5) months with a roughly equal sex distribution; 77 males (51·3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98·3% with an ensemble. Grad-CAMs demonstrated models\u27 ability to learn different microscopic morphological features for EE, CD, and controls.Conclusion: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision making process. Grad-CAMs illuminated the otherwise \u27black box\u27 of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice

Deep learning for detecting diseases in gastrointestinal biopsy images

Machine learning and computer vision have found applications in medical science and, recently, pathology. In particular, deep learning methods for medical diagnostic imaging can reduce delays in diagnosis and give improved accuracy rates over other analysis techniques. This paper focuses on methods with applicability to automated diagnosis of images obtained from gastrointestinal biopsies. These deep learning techniques for biopsy images may help detect distinguishing features in tissues affected by enteropathies. Learning from different areas of an image, or looking for similar patterns in new images, allow for the development of potential classification or clustering models Techniques like these provide a cutting-edge solution to detecting anomalies. In this paper we explore state of the art deep learning architectures used for the visual recognition of natural images and assess their applicability in medical image analysis of digitized human gastrointestinal biopsy slides

Deep learning for visual recognition of environmental enteropathy and celiac disease

Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model