Accurate classification of glomerular diseases by hyperspectral imaging and transformer.

BACKGROUND AND OBJECTIVE: In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsistent results. There is a pressing need for innovative diagnostic tools that enhance traditional methods, streamline processes, and ensure accurate and consistent disease detection. METHODS: In this study, we present an innovative Convolutional Neural Networks-Vision Transformer (CVT) model leveraging Transformer technology to refine glomerular disease diagnosis by fusing spectral and spatial data, surpassing traditional diagnostic limitations. Using interval sampling, preprocessing, and wavelength optimization, we also introduced the Gramian Angular Field (GAF) method for a unified representation of spectral and spatial characteristics. RESULTS: We captured hyperspectral images ranging from 385.18 nm to 1009.47 nm and employed various methods to extract sample features. Initial models based solely on spectral features achieved a accuracy of 85.24 %. However, the CVT model significantly outperformed these, achieving an average accuracy of 94 %. This demonstrates the model's superior capability in utilizing sample data and learning joint feature representations. CONCLUSIONS: The CVT model not only breaks through the limitations of existing diagnostic techniques but also showcases the vast potential of non-invasive, high-precision diagnostic technology in supporting the classification and prognosis of complex glomerular diseases. This innovative approach could significantly impact future diagnostic strategies in renal disease research. CONCISE ABSTRACT: This study introduces a transformative hyperspectral image classification model leveraging a Transformer to significantly improve glomerular disease diagnosis accuracy by synergizing spectral and spatial data, surpassing conventional methods. Through a rigorous comparative analysis, it was determined that while spectral features alone reached a peak accuracy of 85.24 %, the novel Convolutional Neural Network-Transformer (CVT) model's integration of spatial-spectral features via the Gramian Angular Field (GAF) method markedly enhanced diagnostic precision, achieving an average accuracy of 94 %. This methodological innovation not only overcomes traditional diagnostic limitations but also underscores the potential of non-invasive, high-precision technologies in advancing the classification and prognosis of complex renal diseases, setting a new benchmark in the field.

Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma.

BACKGROUND: Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becomes a significant challenge due to their indistinguishable traits. Traditional skin histological examination techniques, largely reliant on light microscopic imagery, offer constrained information and yield low-contrast results, underscoring the necessity for swift and effective early diagnostic methodologies. As a non-contact, non-ionizing, and label-free imaging tool, hyperspectral imaging offers potential in assisting pathologists with identification procedures sans contrast agents. METHODS: This investigation leverages hyperspectral cameras to ascertain the optical properties and to capture the spectral features of malignant melanoma and pigmented nevus tissues, intending to facilitate early pathological diagnostic applications. We further enhance the diagnostic process by integrating transfer learning with deep convolutional networks to classify melanomas and pigmented nevi in hyperspectral pathology images. The study encompasses pathological sections from 50 melanoma and 50 pigmented nevus patients. To accurately represent the spectral variances between different tissues, we employed reflectance calibration, highlighting that the most distinctive spectral differences emerged within the 500-675 nm band range. RESULTS: The classification accuracy of pigmented tumors and pigmented nevi was 89% for one-dimensional sample data and 98% for two-dimensional sample data. CONCLUSIONS: Our findings have the potential to expedite pathological diagnoses, enhance diagnostic precision, and offer novel research perspectives in differentiating melanoma and nevus.

Comparison of the Biological and Chemical Synthesis of Schwertmannite at a Consistent Fe2+ Oxidation Efficiency and the Effect of Extracellular Polymeric Substances of Acidithiobacillus ferrooxidans on Biomineralization.

Schwertmannite is an environmental mineral material that can promote the natural passivation of heavy metal elements, thereby reducing environmental pollution from toxic elements. However, the fundamental reason for the difference between the chemically (H2O2-FeSO4) and biologically (Acidithiobacillus ferrooxidans-FeSO4) synthesized schwertmannite is still unclear. In this study, X-ray diffraction, scanning electron microscopy, the Brunauer⁻Emmett⁻Teller method, and X-ray fluorescence spectrometry were used to compare the structure, specific surface area, and elemental composition of schwertmannite synthesized by biological and chemical methods. The removal capacity of As(III) by the two kinds of schwertmannite and the effects of extracellular polymeric substances (EPS) on biogenetic schwertmannite were also investigated. At a consistent Fe2+ oxidation efficiency, the chemical method synthesized more schwertmannite than the biological method over a 60-h period. The biosynthesized schwertmannite had a "chestnut shell" shape, with a larger particle size and specific surface than the chemically synthesized schwertmannite, which was relatively smooth. The saturated adsorption capacities of the biologically and chemically synthesized schwertmannite were 117.0 and 87.0 mg·g-1, respectively. After exfoliation of the EPS from A. ferrooxidans, the biosynthesized schwertmannite displayed a "wool ball" shape, with rough particle surfaces, many microporous structures, and a larger specific surface area. The schwertmannite yield also increased by about 45% compared with that before exfoliation, suggesting that the secretion of EPS by A. ferrooxidans can inhibit the formation of schwertmannite.