The psychology and policy of overcoming economic inequality.

Recent arguments claim that behavioral science has focused - to its detriment - on the individual over the system when construing behavioral interventions. In this commentary, we argue that tackling economic inequality using both framings in tandem is invaluable. By studying individuals who have overcome inequality, "positive deviants," and the system limitations they navigate, we offer potentially greater policy solutions.

Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image.

Significance: Quantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff's van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming. Aim: The goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Approach: H&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one. Results: The experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber. Conclusions: RGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one.

The persistence of cognitive biases in financial decisions across economic groups.

While economic inequality continues to rise within countries, efforts to address it have been largely ineffective, particularly those involving behavioral approaches. It is often implied but not tested that choice patterns among low-income individuals may be a factor impeding behavioral interventions aimed at improving upward economic mobility. To test this, we assessed rates of ten cognitive biases across nearly 5000 participants from 27 countries. Our analyses were primarily focused on 1458 individuals that were either low-income adults or individuals who grew up in disadvantaged households but had above-average financial well-being as adults, known as positive deviants. Using discrete and complex models, we find evidence of no differences within or between groups or countries. We therefore conclude that choices impeded by cognitive biases alone cannot explain why some individuals do not experience upward economic mobility. Policies must combine both behavioral and structural interventions to improve financial well-being across populations.

Effects of dimension reduction of hyperspectral images in skin gross pathology.

BACKGROUND: Hyperspectral imaging (HSI) is an emerging modality for the gross pathology of the skin. Spectral signatures of HSI could discriminate malignant from benign tissue. Because of inherent redundancies in HSI and in order to facilitate the use of deep-learning models, dimension reduction is a common preprocessing step. The effects of dimension reduction choice, training scope, and number of retained dimensions have not been evaluated on skin HSI for segmentation tasks. MATERIALS AND METHODS: An in-house dataset of HSI signatures from pigmented skin lesions was prepared and labeled with histology. Eleven different dimension reduction methods were used as preprocessing for tumor margin detection with support vector machines. Cluster-wise principal component analysis (ClusterPCA), a new variant of PCA, was proposed. The scope of application for dimension reduction was also investigated. RESULTS: The components produced by ClusterPCA show good agreement with the expected optical properties of skin chromophores. Random forest importance performed best during classification. However, all methods suffered from low sensitivity and generalization. CONCLUSION: Investigation of more complex reduction and segmentation schemes with emphasis on the nature of HSI and optical properties of the skin is necessary. Insights on dimension reduction for skin tissue could facilitate the development of HSI-based systems for cancer margin detection at gross level.

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan.

Purpose: Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen's Survey of Living Conditions were analyzed using interpretable machine learning methods. Methods: Seven interpretable machine learning methods were used to analysis Japan citizens' census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi's quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors. Results: Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age. Conclusions: New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

Hyperspectral imaging for tumor segmentation on pigmented skin lesions.

Significance: Malignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures. Aim: Tumor segmentation of medical HSI data is a research field. The goal of this study is to propose a framework for HSI-based tumor segmentation of PSL. Approach: An HSI dataset of 28 PSL was prepared. Two frameworks for data preprocessing and tumor segmentation were proposed. Models based on machine learning and deep learning were used at the core of each framework. Results: Cross-validation performance showed that pixel-wise processing achieves higher segmentation performance, in terms of the Jaccard coefficient. Simultaneous use of spatio-spectral features produced more comprehensive tumor masks. A three-dimensional Xception-based network achieved performance similar to state-of-the-art networks while allowing for more detailed detection of the tumor border. Conclusions: Good performance was achieved for melanocytic lesions, but margins were difficult to detect in some cases of basal cell carcinoma. The frameworks proposed in this study could be further improved for robustness against different pathologies and detailed delineation of tissue margins to facilitate computer-assisted diagnosis during gross pathology.

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review.

SIGNIFICANCE: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level. AIM: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue. APPROACH: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified. RESULTS: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems. CONCLUSIONS: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

Design of a Hyper-Spectral Imaging System for Gross Pathology of Pigmented Skin Lesions.

Pigmented skin lesions (PSL) are prevalent in Asian populations and their gross pathology remains a manual, tedious task. Hyper-spectral imaging (HSI) is a non-invasive non-ionizing acquisition technique, allowing malignant tissue to be identified by its spectral signature. We set up a hyper-spectral imaging (HSI) system targeting cancer margin detection of PSL. Because classification among PSL is achieved via comparison of spectral signatures, appropriate calibration is necessary to ensure sufficient data quality. We propose a strategy for system building, calibration and pre-processing, under the requirements of fast acquisition and wide field of view. Preliminary results show that the HSI-based system is able to effectively resolve reflectance signatures of ex-vivo tissue.Clinical Relevance-The imaging system proposed in this study can recover reflectance spectra from PSL during gross pathology, providing a wide imaging area.

Generation of attenuation correction factors from time-of-flight PET emission data using high-resolution residual U-net.

Attenuation correction of annihilation photons is essential in PET image reconstruction for providing accurate quantitative activity maps. In the absence of an aligned CT device to obtain attenuation information, we propose the high-resolution residual U-net (HRU-Net) to extract attenuation correction factors (ACF) directly from time-of-flight (TOF) PET emission data. HRU-Net is built upon the U-Net encoding-decoding architecture and it utilizes four blocks of modified residual connections in each stage. In each residual block, concatenation is performed to incorporate input and output feature vectors. In addition, flexible and efficient elements of convolutional neural network (CNN) such as dilated convolutions, pre-activation order of a batch normalization (BN) layer, a rectified linear unit (ReLU) layer and a convolution layer, and residual connections are utilized to extract high resolution features. To illustrate the effectiveness of the proposed method, HRU-Net estimated ACF, attenuation maps and activity maps are compared with maximum likelihood ACF (MLACF) algorithm, U-Net, and HC-Net. An ablation study is conducted using non-TOF and TOF sinograms as inputs of networks. The experimental results show that HRU-Net with TOF projections as inputs leads to normalized root mean square error (NRMSE) of 4.84% ± 1.58%, outperforming MLACF, U-Net and HC-Net with NRMSE of 47.82% ± 13.62%, 6.92% ± 1.94%, and 7.99% ± 2.49%, respectively.

Patient Consent Management by a Purpose-Based Consent Model for Electronic Health Record Based on Blockchain Technology.

OBJECTIVES: Currently, patients' consent is essential to use their medical records for various purposes; however, most people give their consent using paper forms and have no control over it. Healthcare organizations also have difficulties in dealing with patient consent. The objective of this research is to develop a system for patients to manage their consent flexibly and for healthcare organizations to obtain patient consent efficiently for a variety of purposes. METHODS: We introduce a new e-consent model, which uses a purpose-based access control scheme; it is implemented by a blockchain system using Hyperledger Fabric. All metadata of patient records, consents, and data access are written immutably on the blockchain and shared among participant organizations. We also created a blockchain chaincode that performs business logic managing patient consent. RESULTS: We developed a prototype and checked business logics with the chaincode by validating doctors' data access with purpose-based consent of patients stored in the blockchain. The results demonstrate that our system provides a fine-grained way of handling medical staff 's access requests with diverse intended purposes for accessing data. In addition, patients can create, update, and withdraw their consents in the blockchain. CONCLUSIONS: Our consent model is a solution for consent management both for patients and healthcare organizations. Our system, as a blockchain-based solution that provides high reliability and availability with transparency and traceability, is expected to be used not only for patient data sharing in hospitals, but also for data donation for biobank research purposes.

Telepathology Support System with Gross Specimen Image Using High Resolution 4K Multispectral Camera.

This study reports on the development of a high-resolution 4K multispectral camera designed to enhance telepathology support systems for remote gross-pathological diagnosis. We experimentally examine and evaluate the camera's effectiveness in three subjects: the reconstruction of precise color images, the emphasis of cancerous tissue areas, and pre-fixed image reproduction from fixed images. The evaluation results of the first and second subjects showed that the camera and supporting methods could be effectively used in gross pathology diagnosis. The images obtained in the third subject received positive evaluations from some pathologists, but others expressed reservations as to its utility.

Application of Blockchain to Maintaining Patient Records in Electronic Health Record for Enhanced Privacy, Scalability, and Availability.

OBJECTIVES: Electronic Health Record (EHR) systems are increasingly used as an effective method to share patients' records among different hospitals. However, it is still a challenge to access scattered patient data through multiple EHRs. Our goal is to build a system to access patient records easily among EHRs without relying on a centralized supervisory system. METHODS: We apply consortium blockchain to compose a distributed system using Hyperledger Fabric incorporating existent EHRs. Peer nodes hold the same ledger on which the address of a patient record in an EHR is written. Individual patients are identified by unique certificates issued by a local certificate authorities that collaborate with each other in a channel of the network. To protect a patient's privacy, we use a proxy re-encryption scheme when the data are transferred. We designed and implemented various chaincodes to handle business logic agreed by member organizations of the network. RESULTS: We developed a prototype system to implement our concept and tested its performance including chaincode logic. The results demonstrated that our system can be used by doctors to find patient's records and verify patient's consent on access to the data. Patients also can seamlessly receive their past records from other hospitals. The access log is stored transparently and immutably in the ledger that is used for auditing purpose. CONCLUSIONS: Our system is feasible and flexible with scalability and availability in adapting to existing EHRs for strengthening security and privacy in managing patient records. Our research is expected to provide an effective method to integrate dispersed patient records among medical institutions.

Privacy Enhanced Healthcare Information Sharing System for Home-Based Care Environments.

OBJECTIVES: Home-based nursing care services have increased over the past decade. However, accountability and privacy issues as well as security concerns become more challenging during care provider visits. Because of the heterogeneous combination of mobile and stationary assistive medical care devices, conventional systems lack architectural consistency, which leads to inherent time delays and inaccuracies in sharing information. The goal of our study is to develop an architecture that meets the competing goals of accountability and privacy and enhances security in distributed home-based care systems. METHODS: We realized this by using a context-aware approach to manage access to remote data. Our architecture uses a public certification service for individuals, the Japanese Public Key Infrastructure and Health Informatics-PKI to identify and validate the attributes of medical personnel. Both PKI mechanisms are provided by using separate smart cards issued by the government. RESULTS: Context-awareness enables users to have appropriate data access in home-based nursing environments. Our architecture ensures that healthcare providers perform the needed home care services by accessing patient data online and recording transactions. CONCLUSIONS: The proposed method aims to enhance healthcare data access and secure information delivery to preserve user's privacy. We implemented a prototype system and confirmed its feasibility by experimental evaluation. Our research can contribute to reducing patient neglect and wrongful treatment, and thus reduce health insurance costs by ensuring correct insurance claims. Our study can provide a baseline towards building distinctive intelligent treatment options to clinicians and serve as a model for home-based nursing care.

Classification of Elastic and Collagen Fibers in H&E Stained Hyperspectral Images.

This study investigates the classification performance of elastic and collagen fibers using H&E stained hyperspectral images. As many as 1200 sample pixels were trained by using Linear discriminant analysis (LDA) and support vector machine (SVM) methods for reduction and classification. The classification result both using LDA and SVM show that H&E stained hyperspectral images performed better classification than H&E stained RGB image by comparing the classification of EVG stained images visually and quantitatively.

Restoration of lost frequency in OpenPET imaging: comparison between the method of convex projections and the maximum likelihood expectation maximization method.

We are developing a new PET scanner based on the "OpenPET" geometry, which consists of two detector rings separated by a gap. One item to which attention must be paid is that OpenPET image reconstruction is classified into an incomplete inverse problem, where low-frequency components are truncated. In our previous simulations and experiments, however, the OpenPET imaging was made feasible by application of iterative image reconstruction methods. Therefore, we expect that iterative methods have a restorative effect to compensate for the lost frequency. There are two types of reconstruction methods for improving image quality when data truncation exists: one is the iterative methods such as the maximum-likelihood expectation maximization (ML-EM) and the other is an analytical image reconstruction method followed by the method of convex projections, which has not been employed for the OpenPET. In this study, therefore, we propose a method for applying the latter approach to the OpenPET image reconstruction and compare it with the ML-EM. We found that the proposed analytical method could reduce the occurrence of image artifacts caused by the lost frequency. A similar tendency for this restoration effect was observed in ML-EM image reconstruction where no additional restoration method was applied. Therefore, we concluded that the method of convex projections and the ML-EM had a similar restoration effect to compensate for the lost frequency.

Monte Carlo simulation of sensitivity and NECR of an entire-body PET scanner.

The current positron emission tomography (PET) design is aimed toward establishing an entire-body PET scanner. An entire-body PET scanner is a scanner whose axial field of view (FOV) covers the whole body of a patient, whereas whole-body PET scanner can be of any axial FOV length, but was designed for a whole-body scan. Despite its high production cost, an entire-body depth-of-interaction PET scanner offers many benefits, such as shorter and dynamic PET time acquisition, as well as higher sensitivity and count rate performance. This PET scanner may be cost-effective for clinical PET scanners with high scan throughput. In this work, we evaluated the sensitivity and count rate performance of a 2-m-long PET scanner with conventional data acquisition (DAQ) architecture, using Monte Carlo simulation, and we evaluated two ring diameters (60 and 80 cm) to reduce the scanner cost. From simulation of scanning with a 2-m axial FOV, the sensitivity for a 2-m-long PET scanner of 60 and 80-cm diameter is around 80 and 68 times higher, respectively, than that of the conventional PET scanner. In addition, for the 2-m-long PET scanner with 60-cm diameter, the peak noise equivalent count rate (NECR) was 843 kcps at 125 MBq, whereas the peak for the 80-cm diameter was 989 kcps at 200 MBq. This shows gains of 15.3 and 17.95, respectively, in comparison with that of the conventional PET scanner. The 2-m-long PET scanner with 60-cm ring diameter could not only reduce the number of detectors by 21 %, but also had a 17 % higher sensitivity compared to that with an 80-cm ring diameter. On the other hand, despite the higher sensitivity, the NECR of the 60-cm ring diameter was smaller than that of the 80-cm ring diameter. This results from the single data loss due to dead time, whereas grouping of axially stacked detectors was used in the conventional DAQ architecture. Parallelization of the DAQ architecture is therefore important for the 2-m-long PET scanner to achieve its optimal performance.

Secure biometric image sensor and authentication scheme based on compressed sensing.

It is important to ensure the security of biometric authentication information, because its leakage causes serious risks, such as replay attacks using the stolen biometric data, and also because it is almost impossible to replace raw biometric information. In this paper, we propose a secure biometric authentication scheme that protects such information by employing an optical data ciphering technique based on compressed sensing. The proposed scheme is based on two-factor authentication, the biometric information being supplemented by secret information that is used as a random seed for a cipher key. In this scheme, a biometric image is optically encrypted at the time of image capture, and a pair of restored biometric images for enrollment and verification are verified in the authentication server. If any of the biometric information is exposed to risk, it can be reenrolled by changing the secret information. Through numerical experiments, we confirm that finger vein images can be restored from the compressed sensing measurement data. We also present results that verify the accuracy of the scheme.

Multispectral image enhancement for effective visualization.

Color enhancement of multispectral images is useful to visualize the image's spectral features. Previously, a color enhancement method, which enhances the feature of a specified spectral band without changing the average color distribution, was proposed. However, sometimes the enhanced features are indiscernible or invisible, especially when the enhanced spectrum lies outside the visible range. In this paper, we extended the conventional method for more effective visualization of the spectral features both in visible range and non-visible range. In the proposed method, the user specifies both the spectral band for extracting the spectral feature and the color for visualization respectively, so that the spectral feature is enhanced with arbitrary color. The proposed color enhancement method was applied to different types of multispectral images where its effectiveness to visualize spectral features was verified.

Known plaintext attack on double random phase encoding using fingerprint as key and a method for avoiding the attack.

We have shown that the application of double random phase encoding (DRPE) to biometrics enables the use of biometrics as cipher keys for binary data encryption. However, DRPE is reported to be vulnerable to known-plaintext attacks (KPAs) using a phase recovery algorithm. In this study, we investigated the vulnerability of DRPE using fingerprints as cipher keys to the KPAs. By means of computational experiments, we estimated the encryption key and restored the fingerprint image using the estimated key. Further, we propose a method for avoiding the KPA on the DRPE that employs the phase retrieval algorithm. The proposed method makes the amplitude component of the encrypted image constant in order to prevent the amplitude component of the encrypted image from being used as a clue for phase retrieval. Computational experiments showed that the proposed method not only avoids revealing the cipher key and the fingerprint but also serves as a sufficiently accurate verification system.

Feasibility study of near-infrared fluorescence tomography using a positron emission tomograph equipped with depth-of-interaction PET detectors.

We are currently developing an imaging system that combines simultaneous positron emission tomography (PET) with near-infrared (NIR) optical tomography, thus supporting two different types of molecular imaging. For this system, we are considering whether to use depth of interaction (DOI) PET detectors as simultaneous detectors of gamma rays and NIR light by changing the original upper reflectors to dichroic mirrors. The DOI-PET detector has very low spatial resolution for NIR light compared to the charge-coupled device cameras that are normally used. However, it is possible to reconstruct images of comparable value from the data acquired by low-resolution devices because the light is scattered by biological tissues and high-resolution devices are not necessarily effective at improving image quality. In this study, we demonstrate the feasibility of 3D NIR fluorescence tomography imaging by employing DOI-PET detectors in computer simulations. In the simulations, we used a 40 mm x 40 mm x 40 mm cubic phantom, a square detector geometry, and an optical diffusion equation to approximate the light propagation. We then evaluated imaging systems for 3D fluorescence tomography with different detector resolutions and excitation light arrangements using singular-value analysis and imaging simulation. We confirmed that the reconstructed images from low-resolution detectors (8 x 8 pixels for an area of 40 mm x 40 mm) are the same as those from high-resolution detectors (16 x 16 pixels for the same area).