[Deep learning-based fully automated intelligent and precise diagnosis for melanocytic lesions].

Melanocytic lesions occur on the surface of the skin, in which the malignant type is melanoma with a high fatality rate, seriously endangering human health. The histopathological analysis is the gold standard for diagnosis of melanocytic lesions. In this study, a fully automated intelligent diagnosis method based on deep learning was proposed to classify the pathological whole slide images (WSI) of melanocytic lesions. Firstly, the color normalization based on CycleGAN neural network was performed on multi-center pathological WSI; Secondly, ResNet-152 neural network-based deep convolutional network prediction model was built using 745 WSI; Then, a decision fusion model was cascaded, which calculates the average prediction probability of each WSI; Finally, the diagnostic performance of the proposed method was verified by internal and external test sets containing 182 and 54 WSI, respectively. Experimental results showed that the overall diagnostic accuracy of the proposed method reached 94.12% in the internal test set and exceeded 90% in the external test set. Furthermore, the color normalization method adopted was superior to the traditional color statistics-based and staining separation-based methods in terms of structure preservation and artifact suppression. The results demonstrate that the proposed method can achieve high precision and strong robustness in pathological WSI classification of melanocytic lesions, which has the potential in promoting the clinical application of computer-aided pathological diagnosis.

Deep learning-based fully automated differential diagnosis of eyelid basal cell and sebaceous carcinoma using whole slide images.

Background: The differential diagnosis of eyelid basal cell carcinoma (BCC) and sebaceous carcinoma (SC) is highly dependent on pathologist's experience. Herein, we proposed a fully automated differential diagnostic method, which used deep learning (DL) to accurately classify eyelid BCC and SC based on whole slide images (WSIs). Methods: We used 116 haematoxylin and eosin (H&E)-stained sections from 116 eyelid BCC patients and 180 H&E-stained sections from 129 eyelid SC patients treated at the Shanghai Ninth People's Hospital from 2017 to 2019. The method comprises two stages: patch prediction by the DenseNet-161 architecture-based DL model and WSI differentiation by an average-probability strategy-based integration module, and its differential performance was assessed by the carcinoma differentiation accuracy and F1 score. We compared the classification performance of the method with that of three pathologists, two junior and one senior. To validate the auxiliary value of the method, we compared the pathologists' BCC and SC classification with and without the assistance of our proposed method. Results: Our proposed method achieved an accuracy of 0.983, significantly higher than that of the three pathologists (0.644 and 0.729 for the two junior pathologists and 0.831 for the senior pathologist). With the method's assistance, the pathologists' accuracy increased significantly (P<0.05), by 28.8% and 15.2%, respectively, for the two junior pathologists and by 11.8% for the senior pathologist. Conclusions: Our proposed method accurately classifies eyelid BCC and SC and effectively improves the diagnostic accuracy of pathologists. It may therefore facilitate the development of appropriate and timely therapeutic plans.

Deep learning-based fully automated diagnosis of melanocytic lesions by using whole slide images.

BACKGROUND: Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans. OBJECTIVE: To propose a deep learning (DL)-based fully automated diagnostic method using whole slide images (WSIs) for melanocytic lesions. METHODS: The method consisted of patch prediction using a DL model and patient diagnosis using an aggregation module. The method was developed with 745 WSIs and evaluated using internal and external testing sets comprising 182 WSIs and 54 WSIs, respectively. The results were compared with those of the classification by one junior and two senior pathologists. Furthermore, we compared the performance of the three pathologists in the classification of melanocytic lesions with and without the assistance of our method. RESULTS: The method achieved an accuracy of 0.963 and 0.930 on the internal and external testing sets, respectively, which was significantly higher than that of the junior pathologist (0.419 and 0.535). With assistance from the method, all three pathologists achieved higher accuracy on the internal and external testing sets; the accuracy of the junior pathologist increased by 39.0% and 30.2%, respectively (p < .05). CONCLUSION: This generalizable method can accurately classify melanocytic lesions and effectively improve the diagnostic accuracy of pathologists.

PV-MBLL algorithm for extraction of absolute tissue oxygenation information by diffuse optical spectroscopy.

BACKGROUND AND OBJECTIVE: Tissue blood oxygenation contains critical information for biomedical studies and healthcare. The primary approach to extract the absolute value of tissue blood oxygenation (e.g., oxygen saturation) is spatial-resolved algorithm for near-infrared diffuse optical spectroscopy with continues-wave (CW) light, which require acquisition of the optical signals from multiple pairs of sources and detectors (S-D). This study reports the first attempt for absolute oxygenation measurement with single S-D pair of optical signals. METHODS: A novel algorithm, namely, phantom-validation modified Beer-Lambert law (PV-MBLL), was created to fully utilize the optical signals from single S-D pair. This algorithm is combined with two-step phantom measurement to extract the absolute value of tissue oxygenation in CW system. The proposed PV-MBLL algorithm was compared with the conventional spatial-resolved algorithm on both step-varied liquid phantom and human experiment of cuff occlusion on arms. The one-way ANOVA analysis was performed to investigate the difference between the two algorithms. RESULTS: By using the PV-MBLL algorithm, the reconstructed tissue absorption coefficient is highly accurate (not larger than 5.35% in error) over a wide range (0.02-0.20 cm-1). By contrast, the spatial-resolved algorithm leads to much larger errors (up to 37.57% in error). Moreover, the responses of oxygen saturation to cuff occlusion differ significantly (p < 0.005) with the two algorithms. CONCLUSIONS: The proposed PV-MBLL algorithm has promising potential for accurate acquisition of oxygenation information. Additionally, the single S-D pair greatly reduces the size of optical probe and instrument cost, thus it is highly appropriate for the tissues with small size and large curvature.