Low SNR multimirror Fabry-Perot pressure sensor optic spectrum signal analysis and demodulation via SVM-KNN regressors.

We demonstrate an ensemble learning based method to solve the problem of low SNR Fabry-Perot sensor spectrum signal demodulation. Taking the eight-layer approximate coefficients of a multilevel discrete wavelet transform as input features, an ensemble model that combines multiple SVM and KNN learners is trained. Bootstrap and booting techniques are introduced for better modeling performance and stability. It is shown that the performance of the proposed ensemble model based on SVM-KNN regressors is excellent; an accuracy of 0.46%F.S. relative mean error is achieved. This method could provide insight into the construction of a large scale fiber based Fabry-Perot sensor network.

Skin lesion classification based on two-modal images using a multi-scale fully-shared fusion network.

BACKGROUND AND OBJECTIVE: Due to the complexity of skin lesion features, computer-aided diagnosis of skin diseases based on multi-modal images is considered a challenging task. Dermoscopic images and clinical images are commonly used to diagnose skin diseases in clinical scenarios, and the complementarity of their features promotes the research of multi-modality classification in the computer-aided diagnosis field. Most current methods focus on the fusion between modalities and ignore the complementary information within each of them, which leads to the loss of the intra-modality relation. Multi-modality models for integrating features both within single modalities and across multiple modalities are limited in the literature. Therefore, a multi-modality model based on dermoscopic and clinical images is proposed to address this issue. METHODS: We propose a Multi-scale Fully-shared Fusion Network (MFF-Net) that gathers features of dermoscopic images and clinical images for skin lesion classification. In MFF-Net, the multi-scale fusion structure combines deep and shallow features within individual modalities to reduce the loss of spatial information in high-level feature maps. Then Dermo-Clinical Block (DCB) integrates the feature maps from dermoscopic images and clinical images through channel-wise concatenation and using a fully-shared fusion strategy that explores complementary information at different stages. RESULTS: We validated our model on a four-class two-modal skin diseases dataset, and proved that the proposed multi-scale structure, the fusion module DCBs, and the fully-shared fusion strategy improve the performance of MFF-Net independently. Our method achieved the highest average accuracy of 72.9% on the 7-point checklist dataset, outperforming the state-of-the-art single-modality and multi-modality methods with an accuracy boost of 7.1% and 3.4%, respectively. CONCLUSIONS: The multi-scale fusion structure demonstrates the significance of intra-modality relations between clinical images and dermoscopic images. The proposed network combined with the multi-scale structure, DCBs, and the fully-shared fusion strategy, can effectively integrate the features of the skin lesions across the two modalities and achieved a promising accuracy among different skin diseases.

A rotation meanout network with invariance for dermoscopy image classification and retrieval.

The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameters, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show that our method outperforms other anti-rotation methods and achieves great improvements in skin disease classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images.

A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists.

BACKGROUND: Psoriasis is a common chronic inflammatory skin disease that causes physical and psychological burden to patients. A Convolutional Neural Network (CNN) focused on dermoscopic images would substantially aid the classification and increase the accuracy of diagnosis of psoriasis. OBJECTIVES: This study aimed to train an efficient deep-learning network to recognize dermoscopic images of psoriasis (and other papulosquamous diseases), improving the accuracy of the diagnosis of psoriasis. METHODS: EfficientNet-B4 architecture was trained with 7033 dermoscopic images from 1166 patients collected from the Department of Dermatology, Peking Union Medical College Hospital (China). We performed a five-fold cross-validation on the training set to compare the classification performance of EfficientNet-B4 over different networks commonly used in previous studies. From the test set, 90 images were used to compare the performance between our four-class model and that of board-certified dermatologists, whose diagnoses and information (e.g., age, titles) were obtained through an online questionnaire. RESULTS: The mean sensitivity and specificity of EfficientNet-B4 on the training set was 0.927± 0.028 and 0.827 ± 0.043 for the two-class task, and 0.889 ± 0.014 and 0.968 ± 0.004 four-class task. The diagnostic sensitivity and specificity of the 230 dermatologists were 0.688 and 0.903 for psoriasis, 0.677 and 0.838 for eczema, 0.669 and 0.953 for lichen planus, and 0.832 and 0.932 for the "others" group, respectively; the diagnostic sensitivity and specificity of our four-class CNN was 0.929 and 0.952 for psoriasis, 0.773 and 0.926 for eczema, 0.933 and 0.960 for lichen planus, and 0.840 and 0.985 for the "others" group, respectively. Both the 230 dermatologists and CNN achieved at least moderate consistency with the reference standard, and there was no significant difference between them (P > 0.05). CONCLUSIONS: The two-classification and four-classification models of psoriasis established in our study could accurately classify papulosquamous skin diseases. They showed generally comparable performances to the average level of dermatologists and would provide a strong support for the diagnosis of psoriasis.

A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment.

Background: Numerous studies have attempted to apply artificial intelligence (AI) in the dermatological field, mainly on the classification and segmentation of various dermatoses. However, researches under real clinical settings are scarce. Objectives: This study was aimed to construct a novel framework based on deep learning trained by a dataset that represented the real clinical environment in a tertiary class hospital in China, for better adaptation of the AI application in clinical practice among Asian patients. Methods: Our dataset was composed of 13,603 dermatologist-labeled dermoscopic images, containing 14 categories of diseases, namely lichen planus (LP), rosacea (Rosa), viral warts (VW), acne vulgaris (AV), keloid and hypertrophic scar (KAHS), eczema and dermatitis (EAD), dermatofibroma (DF), seborrheic dermatitis (SD), seborrheic keratosis (SK), melanocytic nevus (MN), hemangioma (Hem), psoriasis (Pso), port wine stain (PWS), and basal cell carcinoma (BCC). In this study, we applied Google's EfficientNet-b4 with pre-trained weights on ImageNet as the backbone of our CNN architecture. The final fully-connected classification layer was replaced with 14 output neurons. We added seven auxiliary classifiers to each of the intermediate layer groups. The modified model was retrained with our dataset and implemented using Pytorch. We constructed saliency maps to visualize our network's attention area of input images for its prediction. To explore the visual characteristics of different clinical classes, we also examined the internal image features learned by the proposed framework using t-SNE (t-distributed Stochastic Neighbor Embedding). Results: Test results showed that the proposed framework achieved a high level of classification performance with an overall accuracy of 0.948, a sensitivity of 0.934 and a specificity of 0.950. We also compared the performance of our algorithm with three most widely used CNN models which showed our model outperformed existing models with the highest area under curve (AUC) of 0.985. We further compared this model with 280 board-certificated dermatologists, and results showed a comparable performance level in an 8-class diagnostic task. Conclusions: The proposed framework retrained by the dataset that represented the real clinical environment in our department could accurately classify most common dermatoses that we encountered during outpatient practice including infectious and inflammatory dermatoses, benign and malignant cutaneous tumors.

Schwann cells protect against CaMKII- and PKA-dependent Acrylamide-induced Synapsin I phosphorylation.

OBJECTIVES: To explore the effects of Acrylamide (ACR), as well as the influence of Schwann cells (SCs), on the signal transduction pathway and phosphorylation of Synapsin I in a Human neuroblastoma cell line (NB-1). METHODS: NB-1s, NB-1s co-cultured with SCs, and a negative control group (NB-1 cells without ACR) were exposed to gradient concentrations of ACR for 48 h. Cell proliferation and viability were determined by MTT. Protein and mRNA expression levels of typical kinases (i.e., cAMP-dependent protein kinase [PKA], calcium/calmodulin-dependent protein kinase II [CaMKII], and mitogen-activated protein kinase-extracellular signal-regulated kinases [MAPK-Erk]), their phosphorylation status, as well as Synapsin I and its phosphorylation status, were tested by western blotting and polymerase chain reaction, respectively. Further, the effect of SCs on ACR-induced NB-1 cell toxicity was evaluated. RESULTS: (1) The MTT assay showed a sustained, dose- and time-dependent inhibition of NB-1s exposed to ACR. (2) ACR exposure increased the phosphorylation of CaMKII and PKA, which subsequently increased the phosphorylation of Synapsin I (at Serine603 [a substrate site of CaMKII] and Serine9 [a substrate site of PKA]). Pretreatment with CaMKII and PKA inhibitors blocked the ACR-mediated increase in phosphorylation. The above-described results were all significantly different when compared to the control group (p < 0.05). (3) When co-cultured with SCs, ACR-induced NB-1 inhibition was obviously decreased, and the trend of change of phosphorylated CaMKII, PKA, and Synapsin I were changed (first slightly increased and then decreased), which was inconsistent with what we observed in NB-1s cultured alone. CONCLUSIONS: The toxic effects of ACR on neurons may be mediated by CaMKII and PKA-dependent signaling pathways in which Synapsin I may act as a downstream effector. Furthermore, glial cells (SCs) may be able to prevent a certain degree of ACR-induced neuronal damage.