A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM). OBJECTIVE: We aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL). METHODS: A training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated "iDCNN_aMSL" model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models. RESULTS: In the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %). CONCLUSIONS: The iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions.

Image generation by GAN and style transfer for agar plate image segmentation.

BACKGROUND AND OBJECTIVES: Deep learning models and specifically Convolutional Neural Networks (CNNs) are becoming the leading approach in many computer vision tasks, including medical image analysis. Nevertheless, the CNN training usually requires large sets of supervised data, which are often difficult and expensive to obtain in the medical field. To address the lack of annotated images, image generation is a promising method, which is becoming increasingly popular in the computer vision community. In this paper, we present a new approach to the semantic segmentation of bacterial colonies in agar plate images, based on deep learning and synthetic image generation, to increase the training set size. Indeed, semantic segmentation of bacterial colony is the basis for infection recognition and bacterial counting in Petri plate analysis. METHODS: A convolutional neural network (CNN) is used to separate the bacterial colonies from the background. To face the lack of annotated images, a novel engine is designed - which exploits a generative adversarial network to capture the typical distribution of the bacterial colonies on agar plates - to generate synthetic data. Then, bacterial colony patches are superimposed on existing background images, taking into account both the local appearance of the background and the intrinsic opacity of the bacterial colonies, and a style transfer algorithm is used for further improve visual realism. RESULTS: The proposed deep learning approach has been tested on the only public dataset available with pixel-level annotations for bacterial colony semantic segmentation in agar plates. The role of including synthetic data in the training of a segmentation CNN has been evaluated, showing how comparable performances can be obtained with respect to the use of real images. Qualitative results are also reported for a second public dataset in which the segmentation annotations are not provided. CONCLUSIONS: The use of a small set of real data, together with synthetic images, allows obtaining comparable results with respect to using a complete set of real images. Therefore, the proposed synthetic data generator is able to address the scarcity of biomedical data and provides a scalable and cheap alternative to human ground-truth supervision.

Automatic image classification for the urinoculture screening.

Urinary tract infections (UTIs) are considered to be the most common bacterial infection and, actually, it is estimated that about 150 million UTIs occur world wide yearly, giving rise to roughly $6 billion in healthcare expenditures and resulting in 100,000 hospitalizations. Nevertheless, it is difficult to carefully assess the incidence of UTIs, since an accurate diagnosis depends both on the presence of symptoms and on a positive urinoculture, whereas in most outpatient settings this diagnosis is made without an ad hoc analysis protocol. On the other hand, in the traditional urinoculture test, a sample of midstream urine is put onto a Petri dish, where a growth medium favors the proliferation of germ colonies. Then, the infection severity is evaluated by a visual inspection of a human expert, an error prone and lengthy process. In this paper, we propose a fully automated system for the urinoculture screening that can provide quick and easily traceable results for UTIs. Based on advanced image processing and machine learning tools, the infection type recognition, together with the estimation of the bacterial load, can be automatically carried out, yielding accurate diagnoses. The proposed AID (Automatic Infection Detector) system provides support during the whole analysis process: first, digital color images of Petri dishes are automatically captured, then specific preprocessing and spatial clustering algorithms are applied to isolate the colonies from the culture ground and, finally, an accurate classification of the infections and their severity evaluation are performed. The AID system speeds up the analysis, contributes to the standardization of the process, allows result repeatability, and reduces the costs. Moreover, the continuous transition between sterile and external environments (typical of the standard analysis procedure) is completely avoided.