查看更多>>摘要：New research on Oncology -Gynecologic Cancer is the subject of a report. According to news reporting originating from Houston, Texas, by NewsRx correspondents, research stated, "Ovarian cancer dete ction has traditionally relied on a multistep process that includes biopsy, tiss ue staining, and morphological analysis by experienced pathologists. While widel y practiced, this conventional approach suffers from several drawbacks: it is qu alitative, time-intensive, and heavily dependent on the quality of staining." Our news editors obtained a quote from the research from the University of Houst on, "Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, bioc hemically quantitative technology that, when combined with machine learning algo rithms, can eliminate the need for staining and provide quantitative results com parable to traditional histology. However, this technology is slow. This work pr esents a novel approach to MIR photothermal imaging that enhances its speed by a n order of magnitude. This method resolves the longstanding trade-off between im aging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled data sets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squa red error (MSE), structural similarity index (SSIM), and tissue subtype classifi cation accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Ou r statistically robust analysis, based on data from 100 ovarian cancer patient s amples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gyne cological tissue types with segmentation accuracy exceeding 95%."
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