A smart dandruff analysis system and method are provided for analyzing a severity of a subject's dandruff, and the smart dandruff analysis system has operation module, first neural network module, second neural network module, and classification module. The operation module receives a scalp area image of the subject and transforms the scalp area image into a first feature map. The first neural network module, a Convolutional Neural Network model, electrically connects with the operation module for receiving and transforming the scalp area image into a second feature map. The second neural network module, a Transformer model, electrically connecting with the first neural network module for receiving and transforming the second feature map into a third feature map. The classification module electrically connects with the second neural network module for receiving the third feature map and outputting a rating, wherein the rating is to determine the severity of the subject's dandruff.

Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three-dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.

Methods for non-invasive or minimally invasive assessment of epithelial tissue structure are disclosed. Digital imaging and processing are used to identify cell locations. More specifically, an automated algorithm that may be used to identify epithelial tissue structure, and/or to specify the coordinates/locations of cells in the epithelial tissue structure, through non-invasive or minimally invasive imaging, and use of this information to extract values of epithelial structure related parameters are disclosed.

Provided herein are MD Codes, MD DYNA Codes, MD ASA, and Next Human system, and the methods of using them to diagnose and treat aesthetic conditions or disorders.

The present invention relates to a container, which provides various functions, comprising: a body having an article accommodation portion; a cover for opening and closing the article accommodation portion; and a camera mounting part disposed on the body or the cover, wherein a recessed portion for exposing a camera disposed on the camera mounting part can be formed on the cover.

According to various embodiments, a wrinkle detection service providing server for providing a wrinkle detection method based on an artificial intelligence may include a data pre-processor for obtaining a skin image of a user from a skin measurement device and performing pre-processing based on feature points based on the skin image; a wrinkle detector for inputting the skin image pre-processed through the data pre-processing into an artificial neural network and generating a wrinkle probability map corresponding to the skin image; a data post-processor for post-processing the generated wrinkle probability map; and a wrinkle visualization service providing unit for superimposing the post-processed wrinkle probability map on the skin image and providing a wrinkle visualization image to a user terminal of the user.

Scanning a person’s skin allows one to more easily find products that match their skin tone and skin condition. A skin scan can assist one to determine what products are appropriate for them and then purchase them from a retailer. A scanning device is used to scan one or more locations of a person’s skin. For example, two different locations of a person’s face can be scanned, such as the forehead and cheek. An additional third scan may be near the person’s eyes. The scan determines skin tone and skin condition results for the person’s skin. These results can be used to determine and present a listing of products that are appropriate for the person’s skin tone and skin condition.

The present disclosure provides a training method and device of a neural network for medical image processing, a medical image processing method and device, and an electronic apparatus for medical image processing based on a neural network. The training method includes performing a pre-processing process on an original image to obtain a pre-processed image, performing a data-augmenting process on the pre-processed image to obtain an augmented image retaining a pathological feature, the augmented image including at least one image with first resolution and at least one image with second resolution being higher than the first resolution, and training the neural network by selecting the image with first resolution and a part-cropping image from the image with second resolution as training samples.

A system for providing images of a human scalp and associated information for assisting in hair transplant planning, the system includes a support and at least one camera selectively connectable to the support, the camera and/or the support being configured for acquiring at least one, preferably a plurality of scale calibrated overview images of the human scalp of a patient's head, preferably from different predefined angles, optical acquisition means, preferably a video-dermatoscope, being configured for acquiring a plurality of microscopic images within different regions of the scalp, a processing unit configured to process and/or analyse image data provided by the camera means and the optical acquisition means, in particular for measuring areas of the human scalp, identify and measure hair in microscopic images and/or quantitatively plan a hair transplant operation, wherein the processing unit comprises a user interface configured for interacting with a user.

Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.