Background: Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists. Methods and findings: Our whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7–10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17–30 times the number of potential human errors were detected within an average of 1.2 days. Conclusions: The AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis.

BACKGROUND Digitization of histopathological samples into whole slide images (WSIs) with hematoxylin and eosin (H&E) staining enables deep-learning applications for cancer detection. However, the performance of deep learning models in digital pathology is often compromised by variations in the staining protocols, scanner types, and biological differences across samples. To address this issue, a stain augmentation (SA) method was proposed, which learns the color distribution of a training dataset and generates new images using this distribution. Despite these advantages, overreliance on a single or homogeneous domain can generate similarly stained images, limiting the robustness of the model. OBJECTIVE In this study, we developed and validated an SA method based on stain separation that improved classification performance in H&E-stained breast cancer tissues, even when data are obtained from a single domain. METHODS The augmentation method was validated in both classification and segmentation tasks: 1) five domains in Camelyon17 for classification and 2) three domains in cross-organ and cross-scanner adenocarcinoma segmentation (COSAS) for segmentation, which separated the stain vector and stain density from H&E images and perturbed them by considering the counterstaining characteristics of H&E staining. The predictive model was trained for each domain and assessed in the other domains. RESULTS We obtained 348,091 and 6,989 patch images from the Camelyon17 and COSAS datasets, respectively, for image classification and segmentation tasks. Significant differences were observed in the mean color channel values across all domains in both the hue-saturation-value (HSV) and CIELAB color spaces (P < 0.05). The augmentation approach improved performance over the baseline model, yielding area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) values of 0.949 (+0.142) and 0.831 (+0.306), respectively, compared to the baseline model’s values of 0.807 and 0.525. Combining this technique with RandStainNA further enhanced performance, achieving AUROC and AUPRC values of 0.955 (+0.019) and 0.857 (+0.048), compared to RandStainNA alone (0.936 and 0.808). Additionally, the feature extractor trained with this augmentation demonstrated reduced prediction error for color-jittered images compared to other methods, with an average L2 distance of 0.092 and cosine similarity of 0.992. Under substantial color variation, the mixed approach maintained robust classification performance, achieving AUROC and AUPRC values of 0.887 (+0.054) and 0.758 (+0.081), outperforming RandStainNA (0.833 and 0.677) and the augmentation approach alone (0.852 and 0.705) CONCLUSIONS Stain augmentation using stain separation enhanced the performance of deep learning without data dependence in the segmentation and classification of heterogeneous H&E-stained breast cancer images.

Digital pathology has made significant advances in tumor diagnosis and segmentation; however, image variability resulting from tissue preparation and digitization - referred to as domain shift - remains a significant challenge. Variations caused by heterogeneous scanners introduce color inconsistencies that negatively affect the performance of segmentation algorithms. To address this issue, we have developed a joint multitask U-net architecture trained for both segmentation and stain separation. This model isolates the stain matrix and stain density, allowing it to handle color variations and improve generalization across different scanners. On 180 stain images from three different scanners, our model achieved a Dice score of 0.898 and an Intersection Over Union (IoU) score of 0.816, outperforming conventional supervised learning methods by +1.5% and +2.5%, respectively. On external datasets containing images from six different scanners, our model averaged a Dice score and IoU of 0.792. By leveraging our novel approach to stain separation, we improved segmentation accuracy and generalization across diverse histopathological samples. These advances may pave the way for more reliable and consistent diagnostic tools for breast adenocarcinoma.