Invented by Yingying Li, Lvwei WANG, BOE Technology Group Co Ltd

The market for medical image processing methods and devices has witnessed significant growth in recent years, driven by advancements in technology and increasing demand for accurate and efficient diagnostic tools. Medical image processing refers to the use of computer algorithms and software to enhance, analyze, and interpret medical images such as X-rays, CT scans, MRIs, and ultrasound images. One of the key factors driving the growth of this market is the rising prevalence of chronic diseases and the need for early and accurate diagnosis. Medical imaging plays a crucial role in the detection and monitoring of various diseases, including cancer, cardiovascular diseases, and neurological disorders. With the increasing burden of these diseases globally, there is a growing demand for advanced medical image processing methods and devices that can aid in early detection and improve patient outcomes. Advancements in technology have revolutionized medical image processing, enabling healthcare professionals to obtain high-quality images and extract valuable information from them. The development of sophisticated algorithms and software has made it possible to enhance image resolution, reduce noise, and improve image contrast, leading to more accurate diagnoses. Additionally, the integration of artificial intelligence (AI) and machine learning algorithms has further enhanced the capabilities of medical image processing, allowing for automated image analysis, pattern recognition, and predictive modeling. The market for medical image processing methods and devices is also driven by the increasing adoption of digital imaging systems in healthcare facilities. Digital imaging offers several advantages over traditional film-based imaging, including faster image acquisition, easier storage and retrieval, and the ability to share images electronically. This has led to a widespread transition from analog to digital imaging systems, creating a growing need for advanced image processing methods and devices to handle the large volumes of digital medical images generated. Moreover, the growing focus on personalized medicine and precision diagnostics has further fueled the demand for advanced medical image processing methods and devices. These technologies enable healthcare professionals to tailor treatment plans based on individual patient characteristics, leading to improved patient outcomes and reduced healthcare costs. For instance, medical image processing methods can help identify specific biomarkers or genetic markers in medical images, allowing for targeted therapies and personalized treatment approaches. The market for medical image processing methods and devices is highly competitive, with several key players operating in the industry. These companies are constantly investing in research and development to introduce innovative products and gain a competitive edge. Additionally, collaborations between healthcare providers, technology companies, and research institutions are driving the development of novel image processing methods and devices. In conclusion, the market for medical image processing methods and devices is experiencing significant growth due to the increasing prevalence of chronic diseases, advancements in technology, and the shift towards personalized medicine. The integration of AI and machine learning algorithms, along with the transition to digital imaging systems, has further propelled the market forward. As healthcare providers strive to improve diagnostic accuracy and patient outcomes, the demand for advanced medical image processing methods and devices is expected to continue to rise in the coming years.

The BOE Technology Group Co Ltd invention works as follows

The present disclosure includes a method for training a neural net for medical imaging processing, as well as a method for processing medical images based on the neural network. The training method comprises performing a preprocessing on an original image in order to obtain a processed image, performing an image-augmenting on the processed image to produce an augmented picture retaining a feature pathological, the augmented picture including at least an image with a first resolution (and at least an image with a second resolution higher than the first), and training the neural system by selecting an image of first resolution from the image of second resolution.

Background for Medical image processing method and devices

Early detection and accurate diagnoses of skin diseases, particularly melanoma, are crucial. Dermoscopy is also called skin surface microscope. It’s a non-invasive technique that uses microscopic images to observe microstructures and skin pigments. The dermoscopy, also known as skin surface microscope, is a non-invasive microscopic image analysis technique for observing microstructures and pigments under the skin surface of living body.

It should be noted that information in the previous background section may not constitute prior knowledge that is already well known by those with ordinary skill in this art.

The present disclosure relates to a method and device for medical image analysis, as well as a method and apparatus for processing medical images based on neural networks.

The present disclosure provides a method for training a neural net to process medical images. This includes performing a preprocessing on an original image in order to obtain a “pre-processed” image, performing an “augmenting” process on the preprocessed picture to obtain an “augmented” image that retains the pathological feature.

In an arrangement of the current disclosure, performing a preprocessing process on an image original includes performing a pixel-normalizing process and a colour constancy process on the image original.

In an arrangement of the current disclosure, performing a data enhancement process on the image pre-processed includes at least one process to enhance the image pre-processed: cropping, rotating, upside-down, inverting, and horizontal inverting processes.

In an arrangement of the current disclosure, performing a data enhancement process on a pre-processed picture includes also performing an image warping process on that pre-processed picture.

The arrangement described in the present disclosure includes an original image that contains a dermoscopy, a cropping process on the preprocessed images to maintain a boundary around a lesion, and an image-warping on the preprocessed images to retain the symmetrical characteristics of the lesion.

In an arrangement of this disclosure, the part cropping image from the second resolution image includes a centre-cropping, and the same resolution is used for the center-cropping as the first resolution image.

The neural network is trained using the images with the first and second resolutions, as well as a part-cropping from the second resolution.

In an arrangement of this disclosure, the resolutions of the images of the image of second resolution, of the image of third resolution, and of the first resolution image are all the same.

The present disclosure provides a device for training a neural net to process medical images, which includes: a preprocessing part configured to perform preprocessing on an original image in order to obtain a processed image; an augmenting component configured to perform data-augmenting on the preprocessed picture to obtain an enhanced image that retains the pathological feature. This augmented image comprises at least two images, one with a first resolution and one with a second resolution. The second resolution is higher than the resolution of the pre

The arrangement in the present disclosure includes two augmenting parts. The first is configured to perform one or more of the following processes in order to enhance the preprocessed images: cropping, rotating, upside-down and horizontal inverting, while the second is configured for an image warping process.

The neural network is trained by introducing the image of first resolution as well as the part-cropping from the image of second resolution.

In an arrangement of this disclosure, the resolutions of the images of the image of second resolution, of the image of third resolution, and of the first resolution image are all the same.

The present disclosure describes a device that can be used to train a neural network to process medical images. It includes a processor and a memory with computer program instructions. When executed by the processor these instructions cause the processor to execute one or more blocks of the training method.

The present disclosure provides a method of processing medical images based on neural networks, which includes: obtaining a digital medical image; processing it by putting the image into the trained neural network by using the training method above; and displaying the processed result.

In an arrangement of the current disclosure, processing the medical images includes classification of the medical images based on a feature pathological of a lesion.

The present disclosure describes a medical image-processing device that is based on neural networks. It includes: a processor and a storage memory with computer program instructions. When executed by the processor these instructions cause the processor perform one or several blocks of the medical image-processing method.

The computer program instructions, when executed by the processor, cause the processor perform the above method of medical image processing to classify a medical image based upon a pathological characteristic of a lesion in the medical picture.

According to an arrangement in the present disclosure, an electronic apparatus is provided for medical image-processing based on neural networks, which includes: an image-obtaining part configured to obtain a clinical image; an image-processing part configured to process the image using the neural network trained by this training method; and an output part configured to output a result of image processing.

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