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Found 7 papers, 1 papers with code
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation
Blood vessel segmentation is crucial for many diagnostic and research applications.
MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis
It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer.
PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.
Multi-label Detection and Classification of Red Blood Cells in Microscopic Images
As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples.
ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale.
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.
Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice.
