The present application relates to the field of artificial intelligence. Discloses are a semi-supervised learning-based image classification method and apparatus, a computer device and a storage medium. The method comprises: obtaining an OCT image to be classified; processing said OCT image by using a feature vector generator in a preset OCT image classification model to obtain a first feature vector X generated by a first encoder; decoding the first feature vector X by using a first decoder to obtain a decoded image; generating a second feature vector Y by using a second encoder; calculating a similarity value between the first feature vector X and the second feature vector Y, and determining whether the similarity value is greater than a preset similarity threshold; and if the similarity value is greater than the preset similarity threshold, classifying said OCT image as a negative image. Therefore, OCT image classification is completed without positive data, and the defect of difficulty in collecting positive data is overcome.

Semi-supervised learning-based image classification method and apparatus, and computer device

A segmentation model training method, an OCT image segmentation method and apparatus, a device and a medium. The segmentation model training method comprises: acquiring a training sample image set, wherein the training sample image set comprises an original OCT image and a gold standard image (S10); inputting the original OCT image into a pre-set generator model for segmentation processing to obtain a first segmented image (S20); comparing the first segmented image with the gold standard image by means of a pre-set discriminator model to obtain a comparison result, calculating a loss function of the generator model according to the comparison result, and updating the generator model according to the loss function (S30); using the updated generator model to convert the first segmented image into a second segmented image, inputting the second segmented image and the gold standard image into the pre-set discriminator model, and updating the discriminator model according to a binary cross entropy to obtain an updated discriminator model (S40); and carrying out iterative training on the updated generator model and the updated discriminator model until a loss function of the updated discriminator model converges, then stopping the iterative training, and determining the updated generator model after the iterative training is stopped as an image lesion segmentation model (S50). The segmentation model training method improves the performance of a segmentation model by means of adversarial training, and improves the accuracy of the segmentation model.

Segmentation model training method, oct image segmentation method and apparatus, device and medium

Disclosed are an image segmentation model training method and apparatus, an image segmentation method and apparatus, and a device and a medium. The image segmentation model training method comprises: performing down-sampling on an eye fundus image, and inputting a down-sampling result into a region proposal network, so as to obtain regions of interest of different scales and classifications thereof; performing multi-scale fusion segmentation on the regions of interest and regions of interest after up-sampling; and adjusting parameters of down-sampling, the region proposal network and up-sampling according to the value of a loss function until the value of the loss function is within a preset error range.

Image segmentation model training method and apparatus, image segmentation method and apparatus, and device and medium

The present invention relates to the technical field of artificial intelligence. Disclosed are a method and device for denoising an OCT image based on a cyclic generative adversarial network. The method for denoising an OCT image comprises: acquiring an OCT image to be denoised; inputting said OCT image to a cyclic generative adversarial network model obtained from training; and outputting a denoised OCT image by means of the cyclic generative adversarial network model. The present invention employs a cyclic generative adversarial network model to perform denoising processing on an OCT image, such that a high-noise OCT image can be effectively converted to a clear OCT image for examination by a doctor or analysis by software. The invention eliminates the limitation in the prior art in which the application of deep learning in denoising requires pairing of training data, thereby facilitating acquisition of a large amount of data for training, and accordingly improving denoising performance of a model.

Method and device for denoising oct image based on cyclic generative adversarial network

Disclosed are an image processing method and apparatus, a computer device, and a storage medium. The method comprises: randomly obtaining a preset number of multiple training images from a preset training image set; performing, according to a preset spatial transformation network, spatial transformation on the multiple training images to obtain corresponding registration images; performing, according to a preset convolutional noise reduction model, superimposition and noise reduction on the registration images to obtain corresponding noise reduction images; performing iterative training on the convolutional noise reduction model according to a preset gradient descent training model, the registration images, the noise reduction images, and the training image set to obtain a trained convolutional noise reduction model; and processing, according to the spatial transformation network, the trained convolutional noise reduction model, and a preset superimposition rule, an image to be processed input by a user to obtain a corresponding optimized image.

Image processing method and apparatus, computer device, and storage medium

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