Deep learning based medical image detection method and related device

An apparatus for performing a deep learning based medical image detection, the apparatus comprising:

• a memory storing instructions;
• and
• a processor in communication with the memory, wherein, when the instructions are executed by the processor, the processor is configured to cause the apparatus to:
• acquire a to-be-detected medical image comprising a plurality of slices;
• for each slice in the to-be-detected medical image:
• extract N basic feature maps of the slice by a deep neural network, N being an integer greater than 1,
• merge features of the N basic feature maps of the slice by the deep neural network, to obtain M enhanced feature maps of the slice, M being an integer greater than 1, and
• respectively perform a hierarchically dilated convolutions operation on the M enhanced feature maps of the slice by the deep neural network, to generate a superposed feature map of each enhanced feature map of the slice;
• and
• predict position information of a region of interest and a confidence score thereof in the to-be-detected medical image by the deep neural network based on the superposed feature map of each slice in the to-be-detected medical image.

A non-transitory computer readable storage medium storing computer readable instructions, wherein, the computer readable instructions, when executed by a processor, are configured to cause the processor to perform:

• acquiring a to-be-detected medical image comprising a plurality of slices;
• for each slice in the to-be-detected medical image:
• extracting N basic feature maps of the slice by a deep neural network, N being an integer greater than 1,
• merging features of the N basic feature maps of the slice by the deep neural network, to obtain M enhanced feature maps of the slice, M being an integer greater than 1, and
• respectively performing a hierarchically dilated convolutions operation on the M enhanced feature maps of the slice by the deep neural network, to generate a superposed feature map of each enhanced feature map of the slice;
• and
• predicting position information of a region of interest and a confidence score thereof in the to-be-detected medical image by the deep neural network based on the superposed feature map of each slice in the to-be-detected medical image.