Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks has received limited attention. To bridge this gap, we propose Style-Extracting Diffusion Models, featuring two conditioning mechanisms. Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e.g., layout for segmentation. We introduce a trainable style encoder to extract style information from images, and an aggregation block that merges style information from multiple style inputs. This architecture enables the generation of images with unseen styles in a zero-shot manner, by leveraging styles from unseen images, resulting in more diverse generations. In this work, we use the image layout as target condition and first show the capability of our method on a natural image dataset as a proof-of-concept. We further demonstrate its versatility in histopathology, where we combine prior knowledge about tissue composition and unannotated data to create diverse synthetic images with known layouts. This allows us to generate additional synthetic data to train a segmentation network in a semi-supervised fashion. We verify the added value of the generated images by showing improved segmentation results and lower performance variability between patients when synthetic images are included during segmentation training. Our code will be made publicly available at [LINK].
Malaria is a life-threatening disease, which mainly occurs in developing countries and regions with poor sanitary conditions. Early diagnosis of malaria will effectively decrease the death rate. In this article, we develop an automatic malaria disease diagnosis framework integrating blockchain-enabled cloud–edge computing and deep learning. The diagnosis task is divided into malaria parasite segmentation from blood smear images and classification of parasite species and stages. To meet the massive demand for deep learning training, we design a diagnosis pipeline that is deployed in a cloud–edge paradigm to utilize both local and remote resources. At edge nodes, preprocessed data sets are classified by U-Net in a supervised approach to generate coarse probability maps. Then, the normalized images and generated probability maps are uploaded to the cloud server. At the cloud, the uploaded probability maps are used to weakly supervise the stacked dilated U-Net (SDU-Net) to segment infected cells. Further classifications of malaria parasites species and stages are conducted by a pretrained MobileNet V1. The blockchain technology is adopted during the data transmission process. The diagnosis results will be sent back to the original local hospital immediately through the cloud. Our framework improves the diagnosis accuracy and eases the burden of deep learning training. Evaluation on real data collection MP-IDB demonstrated the effectiveness of our method.
Muhammad Umar Nasir, Safiullah Khan, Shahid Mehmood, Muhammad Adnan Khan · 6 authors
Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.
BACKGROUND: Recent advancements in digital pathology resulting from advances in imaging and digitization have increased the convenience and usability of pathology for disease diagnosis, especially in oncology, urology, and gastroenteric diagnosis. However, despite the possibilities to include low-cost diagnosis and viable telemedicine, digital pathology is not yet accessible owing to expensive storage, data security requirements, and network bandwidth limitations to transfer high-resolution images and associated data. The increase in storage, transmission, and security complexity concerning data collection and diagnosis makes it even more challenging to use artificial intelligence algorithms for machine-assisted disease diagnosis. We designed and prototyped a digital pathology system that uses blockchain-based smart contracts using the nonfungible token (NFT) standard and the Interplanetary File System for data storage. Our design remediates shortcomings in the existing digital pathology systems infrastructure, which is centralized. The proposed design is extendable to other fields of medicine that require high-fidelity image and data storage. Our solution is implemented in data systems that can improve access quality of care and reduce the cost of access to specialized pathological diagnosis, reducing cycle times for diagnosis. OBJECTIVE: The main objectives of this study are to highlight the issues in digital pathology and suggest that a software architecture-based blockchain and the Interplanetary File System create a low-cost data storage and transmission technology. METHODS: We used the design science research method consisting of 6 stages to inform our design overall. We innovated over existing public-private designs for blockchains but using a 2-layered approach that separates actual file storage from metadata and data persistence. RESULTS: Here, we identified key challenges to adopting digital pathology, including challenges concerning long-term storage and the transmission of information. Next, using accepted frameworks in NFT-based intelligent contracts and recent innovations in distributed secure storage, we proposed a decentralized, secure, and privacy-preserving digital pathology system. Our design and prototype implementation using Solidity, web3.js, Ethereum, and node.js helped us address several challenges facing digital pathology. We demonstrated how our solution, which combines NFT smart contract standard with persistent decentralized file storage, solves most of the challenges of digital pathology and sets the stage for reducing costs and improving patient care and speed of diagnosis. CONCLUSIONS: We identified technical limitations that increase costs and reduce the mass adoption of digital pathology. We presented several design innovations using NFT decentralized storage standards to prototype a system. We also presented the implementation details of a unique security architecture for a digital pathology system. We illustrated how this design can overcome privacy, security, network-based storage, and data transmission limitations. We illustrated how improving these factors sets the stage for improving data quality and standardized application of machine learning and artificial intelligence to such data.
Diabetes is a serious medical condition and regular screening for diabetes is of great importance as treatment options are most effective in the early stages of diabetes. Digital imaging of retina is considered as a low-cost method for screening and could be used in conjunction with computer-based image processing techniques to automatically detect early signs of diabetes utilizing diabetes-related pathologies visible in retinal fundus images. This research proposes a novel computer-assisted diagnosis (CAD) system for assisting with the screening of the population as up to 50% of the affected population are not aware of having diabetes. Moreover, these screenings are often carried out by an optometrist who receives some training with the patients being referred to an ophthalmologist if they show symptoms. Having a computer-assisted diagnosis system assisting the optometrist during the screening can greatly increase the detection rate for patients with diabetes by providing a second opinion and highlighting any suspicious pathologies. For achieving the highest detection rate possible, a hybrid machine learning approach is proposed in this research by combining Deep Learning with the AdaBoost classifier. The proposed computer-assisted diagnosis system starts with the segmentation of the blood vessels. Then, microaneurysms and exudates are segmentation from the image. Statistical and regional features are then extracted utilizing first, second, and higher-order image features. A Deep Learning framework will be utilized for extracting additional statistical image descriptors as a Deep Learning has superior contextual analysis capabilities compared to other machine learning techniques. Finally, the most informative features are selected by a minimal-redundancy maximal-relevance feature selection approach with an AdaBoost classifier analyzing all the features and informing the operator regarding the patient’s condition. Ethereum Swarm blockchain-based decentralized cloud file storage provides the proposed CAD users with a secure storage olution to access the patient information and related images. The sensitivity, specificity, and accuracy of the classification will be measured under clinical conditions. Healthcare, government, and public users would receive the most benefit from this project.
Vijayasri Iyer, A Vyshnavi, Sriram Iyer, P. K. Krishnan Namboori
In the pharmacogenomic and theranostic approach of treating melanoma, a continuous monitoring of the disease and the mutations associated with the disease is essential. Such a monitoring system has been designed and developed based upon the concept `One-shot learning', a machine learning technique adapted to work with a relatively small number of training images. The samples have been exhaustively studied through genomics, epigenomics, metagenomics and environmental genomics, finding the genetic signature behind proneness of these attributes. The mutations CDK4, CDKN2A, BRAF and KIT have been included in the analysis. The prediction accuracy of the machine is found to substantially high suggesting the device for the theranostic and pharmacogenomic strategies of controlling melanoma. A Distributed Ledger Technology (DLT) based system has been proposed for real time data sharing, training and analysis enabling hospitals and research labs to communicate with each other and conduct a cost-effective diagnostic workflow.