TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images

Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally

Toward Large Scale Semantic Image Understanding and Retrieval

Semantic image retrieval is a multifaceted, highly complex problem. Not only does the solution to this problem require advanced image processing and computer vision techniques, but it also requires knowledge beyond what can be inferred from the image content alone. In contrast, traditional image retrieval systems are based upon keyword searches on filenames or metadata tags, e.g. Google image search, Flickr search, etc. These conventional systems do not analyze the image content and their keywords are not guaranteed to represent the image. Thus, there is significant need for a semantic image retrieval system that can analyze and retrieve images based upon the content and relationships that exist in the real world.In this thesis, I present a framework that moves towards advancing semantic image retrieval in large scale datasets. At a conceptual level, semantic image retrieval requires the following steps: viewing an image, understanding the content of the image, indexing the important aspects of the image, connecting the image concepts to the real world, and finally retrieving the images based upon the index concepts or related concepts. My proposed framework addresses each of these components in my ultimate goal of improving image retrieval. The first task is the essential task of understanding the content of an image. Unfortunately, typically the only data used by a computer algorithm when analyzing images is the low-level pixel data. But, to achieve human level comprehension, a machine must overcome the semantic gap, or disparity that exists between the image data and human understanding. This translation of the low-level information into a high-level representation is an extremely difficult problem that requires more than the image pixel information. I describe my solution to this problem through the use of an online knowledge acquisition and storage system. This system utilizes the extensible, visual, and interactable properties of Scalable Vector Graphics (SVG) combined with online crowd sourcing tools to collect high level knowledge about visual content.I further describe the utilization of knowledge and semantic data for image understanding. Specifically, I seek to incorporate knowledge in various algorithms that cannot be inferred from the image pixels alone. This information comes from related images or structured data (in the form of hierarchies and ontologies) to improve the performance of object detection and image segmentation tasks. These understanding tasks are crucial intermediate steps towards retrieval and semantic understanding. However, the typical object detection and segmentation tasks requires an abundance of training data for machine learning algorithms. The prior training information provides information on what patterns and visual features the algorithm should be looking for when processing an image. In contrast, my algorithm utilizes related semantic images to extract the visual properties of an object and also to decrease the search space of my detection algorithm. Furthermore, I demonstrate the use of related images in the image segmentation process. Again, without the use of prior training data, I present a method for foreground object segmentation by finding the shared area that exists in a set of images. I demonstrate the effectiveness of my method on structured image datasets that have defined relationships between classes i.e. parent-child, or sibling classes.Finally, I introduce my framework for semantic image retrieval. I enhance the proposed knowledge acquisition and image understanding techniques with semantic knowledge through linked data and web semantic languages. This is an essential step in semantic image retrieval. For example, a car class classified by an image processing algorithm not enhanced by external knowledge would have no idea that a car is a type of vehicle which would also be highly related to a truck and less related to other transportation methods like a train . However, a query for modes of human transportation should return all of the mentioned classes. Thus, I demonstrate how to integrate information from both image processing algorithms and semantic knowledge bases to perform interesting queries that would otherwise be impossible. The key component of this system is a novel property reasoner that is able to translate low level image features into semantically relevant object properties. I use a combination of XML based languages such as SVG, RDF, and OWL in order to link to existing ontologies available on the web. My experiments demonstrate an efficient data collection framework and novel utilization of semantic data for image analysis and retrieval on datasets of people and landmarks collected from sources such as IMDB and Flickr. Ultimately, my thesis presents improvements to the state of the art in visual knowledge representation/acquisition and computer vision algorithms such as detection and segmentation toward the goal of enhanced semantic image retrieval

Development of Optical Devices for Digital Medicine

Department of Biomedical EngineeringAdvances of technology have made a revolution that interconnects industrial devices and fuses the boundaries of digital, physical and biological spaces. These technologies such as cloud computing, 3D printing technology, big data, internet of things (IOT), artificial intelligence (AI), and maturity of system integrations have been improved every year, changing our daily life quickly in intelligent and convenient ways. In this days, these explosions of technology, changing the way we live and think, is referred to 4th industrial revolution. As we know, every industry is affected by the new waves of technologies, digitalization and connectivity, and the biomedical or medical field is no exception. Healthcare fields have benefited mostly from recent technical improvements, revolutionizing the medical systems in many terms in cost-effective ways. Particularly, ???digital medicine??? has been recently came into the limelight as one of the uprising fields. In digital medicine, traditional medical devices and diagnostic programs have become miniaturized, digitalized, and automated. As taking advantages of digital medicine, specific fields related to digital pathology, point-of-care (POC) diagnostics, and application of deep learning or machine learning technologies have shown the great potentials not only in biomedical academia but also in the revenues of their markets. It allows to connect devices, hospital equipment, and to accelerate efficiencies in health service such as diagnosis, and to reduce the cost of services. Moreover, interconnection between advanced technologies has been improved the access of healthcare to the places where hospital or medical services are limited. Furthermore, artificial intelligence has shown promising results related to disease screening especially using medical images. Although fields in digital medicine are prospering, still there are limitations that needs to be overcome in order to provide further advanced health services to patients in the various situations. In digital pathology, improvements of microscopic technologies, internets, and storage capabilities have reduced the time-consuming processes. The simple transformation of microscopic image to digital have successfully alternated many limitations in the analogue histopathology workflow to efficient and cost saving ways. However, tissue staining is currently referred as one of the bottleneck that makes workflow still lengthy, labor-intensive, and costly. In the POC diagnostic fields, various digitalized portable smartphone-based diagnostic devices have been introduced as alternatives to conventional medical services. These devices have provided the quality assurance of diagnostics by taking advantages of sharing, and quantitative analysis of digital information. However, most of these works have been focused on replacing diagnostic process which mostly done in laboratory settings. As medical imaging devices and trained clinicians or practitioners are limited, there are also high demands on clinical imaging-based diagnostics in developing countries. In this thesis, computational microscope using patterned NIR illumination was developed for label-free quantitative differential phase tissue imaging to bypass the staining process of the pathology workflow. This system overcame the limitations found in the conventional quantitative differential phase contrast in a LED array microscope, allowing to captured light scattering and absorbing specimen while maintaining weak object approximation. Moreover, portable endoscope system was developed integrating the additive production technologies (3D printing), ICT, and optics for POC diagnostics. This innovative POC endoscope demonstrated comparable imaging capability to that of commercialized clinical endoscope system. Furthermore, deep learning and machine learning models have been trained and applied to each devices, respectively. Generative adversarial network (GAN) was applied to our NIR-based QPI system to virtually stain the label-free QPI which look comparable to image that is captured from bright field microscope using labeled tissue. Lastly, POC automated cervical cancer screening system was developed utilizing smartphone-based endoscope system as well as training the machine learning algorithm. 3-5% of acetic acid was applied to the suspicious lesion and its reaction was captured before and after application using smartphone endoscope. This screening system enables to extract the features of cancers and informs the possibility of cancer from endoscopic images.clos

Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science

This volume is an eclectic mix of applications of Monte Carlo methods in many fields of research should not be surprising, because of the ubiquitous use of these methods in many fields of human endeavor. In an attempt to focus attention on a manageable set of applications, the main thrust of this book is to emphasize applications of Monte Carlo simulation methods in biology and medicine