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Found 5 papers, 5 papers with code
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.
DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease Attention for Multi-Label Lesion Detection in Chest X-rays
However, such networks are difficult to focus on lesion regions in chest X-rays due to their high resemblance in vision.
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision.
Ranked #1 on
Medical Image Segmentation
on SegPC-2021
An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net
Nuclei segmentation is an important step in the task of medical image analysis.
DR-Unet104 for Multimodal MRI brain tumor segmentation
We verified the effect of introducing the regularisation of dropout with small rate (e. g. 0. 2) on the architecture, and found a dropout of 0. 2 improved the overall performance compared to no dropout, or a dropout of 0. 5.
