FEMI, an AI model for IVF, uses 18 million images to improve embryo assessment, offering a non-invasive, cost-effective ...

Experimental results demonstrate that deep-AMPpred performs well in accurately identifying AMPs and predicting their functional activities. This confirms the effectiveness of using the ESM-2 model to ...

Having huge datasets separates the boundaries of two classes and provides a warning. 33 It can also perform in n-dimensional space. Table 1 shows the comparison of the different deep learning methods ...

Particularly, deep neural networks based on U-shaped architectures and skip connections have been extensively employed in various medical image tasks. U-Net is characterized by its encoder-decoder ...

To develop an accurate segmentation model for the prostate and lesion area to help clinicians diagnose diseases, we propose a multi-encoder and decoder segmentation network, denoted Muled-Net, which ...

EMED-UNet: An Efficient Multi-Encoder-Decoder Based UNet for Medical Image Segmentation Abstract: Many current and state-of-the-art deep learning models for accurate image segmentation are based on ...

An asymmetric encoder is used to extract features, and then two decoders segment the brain tumor and reconstruct the input image, respectively. The first decoder outputs the segmentation results from ...