Ovarian cancer detection has traditionally relied on a multi-step process
that includes biopsy, tissue staining, and morphological analysis by
experienced pathologists. While widely practiced, this conventional approach
suffers from several drawbacks: it is qualitative, time-intensive, and heavily
dependent on the quality of staining. Mid-infrared (MIR) hyperspectral
photothermal imaging is a label-free, biochemically quantitative technology
that, when combined with machine learning algorithms, can eliminate the need
for staining and provide quantitative results comparable to traditional
histology. However, this technology is slow. This work presents a novel
approach to MIR photothermal imaging that enhances its speed by an order of
magnitude. Our method significantly accelerates data collection by capturing a
combination of high-resolution and interleaved, lower-resolution infrared band
images and applying computational techniques for data interpolation. We
effectively minimize data collection requirements by leveraging sparse data
acquisition and employing curvelet-based reconstruction algorithms. This method
enables the reconstruction of high-quality, high-resolution images from
undersampled datasets 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 squared error (MSE), structural
similarity index (SSIM), and tissue subtype classification accuracies,
employing both random forest and convolutional neural network (CNN) models,
accompanied by ROC curves. Our statistically robust analysis, based on data
from 100 ovarian cancer patient samples and over 65 million data points,
demonstrates the method's capability to produce superior image quality and
accurately distinguish between different gynecological tissue types with
segmentation accuracy exceeding 95%.

通过稀疏成像方法，将中红外光热成像技术应用于卵巢癌检测，实现了数据采集速度提升 10 倍，并利用随机森林和卷积神经网络模型对超过 65 百万个数据点进行了分析，证明了该方法能够产生优秀的图像质量，并以超过 95% 的分割准确率准确区分不同的妇科组织类型。