Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. Additionally, the number of HSI-based research studies remains limited, and in many cases, the advantages of HSI over traditional RGB imaging have not been conclusively demonstrated, particularly for specimens collected intraoperatively. To address these challenges we present a database consisted of 27 HSIs of hematoxylin-eosin stained frozen sections, collected from 14 patients with colon adenocarcinoma metastasized to the liver. It is aimed to validate pixel-wise classification for intraoperative tumor resection. The HSIs were acquired in the spectral range of 450 to 800 nm, with a resolution of 1 nm, resulting in images of 1384x1035 pixels. Pixel-wise annotations were performed by three pathologists. To overcome challenges such as experimental variability and the lack of annotated data, we combined label-propagation-based semi-supervised learning (SSL) with spectral-spatial features extracted by: the multiscale principle of relevant information (MPRI) method and tensor singular spectrum analysis method. Using only 1% of labeled pixels per class the SSL-MPRI method achieved a micro balanced accuracy (BACC) of 0.9313 and a micro F1-score of 0.9235 on the HSI dataset. The performance on corresponding RGB images was lower, with a micro BACC of 0.8809 and a micro F1-score of 0.8688. These improvements are statistically significant. The SSL-MPRI approach outperformed six DL architectures trained with 63% of labeled pixels. Data and code are available at: https://github.com/ikopriva/ColonCancerHSI.

翻译：高光谱成像（HSI）在变革计算病理学领域方面具有巨大潜力。然而，目前缺乏训练深度学习（DL）模型所需的像素级标注HSI数据。此外，基于HSI的研究数量仍然有限，并且在许多情况下，HSI相较于传统RGB成像的优势尚未得到明确证实，特别是对于术中采集的标本。为应对这些挑战，我们提出了一个数据库，包含27幅来自14名结肠腺癌肝转移患者的苏木精-伊红染色冰冻切片HSI图像。该数据库旨在验证术中肿瘤切除的像素级分类。HSI图像在450至800 nm光谱范围内采集，分辨率为1 nm，生成图像尺寸为1384x1035像素。像素级标注由三位病理学家完成。为克服实验变异性和标注数据缺乏等挑战，我们将基于标签传播的半监督学习（SSL）与通过以下方法提取的光谱-空间特征相结合：多尺度相关信息原理（MPRI）方法和张量奇异谱分析方法。SSL-MPRI方法仅使用每类1%的标注像素，在HSI数据集上实现了0.9313的微平衡准确率（BACC）和0.9235的微F1分数。在相应RGB图像上的性能较低，微BACC为0.8809，微F1分数为0.8688。这些改进具有统计学显著性。SSL-MPRI方法的表现优于使用63%标注像素训练的六种DL架构。数据与代码可在以下网址获取：https://github.com/ikopriva/ColonCancerHSI。