Adaptive critical subgraph mining for cognitive impairment conversion prediction with T1-MRI-based brain network.

BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.

GAMMIL: A graph attention-guided multi-scale fusion multiple instance learning model for the WHO grading of meningioma in whole slide images.

Segmentation outperforms registration in quantitative analysis of brain iron.

HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation.

SaB-Net: Self-attention backward network for gastric tumor segmentation in CT images.

A nutrition-based radiomics-clinical model to predict the prognosis of patients with acute-on-chronic liver failure.

A hybrid classification model with radiomics and CNN for high and low grading of prostate cancer Gleason score on mp-MRI.

3D convolutional network with edge detection for prostate gland and tumor segmentation on T2WI and ADC.

TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma.

Multimodal cross enhanced fusion network for diagnosis of Alzheimer's disease and subjective memory complaints.

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

Design of a rapid diagnostic model for bladder compliance based on real-time intravesical pressure monitoring system.

3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection.

Bilinear pooling and metric learning network for early Alzheimer's disease identification with FDG-PET images.