Results 201 to 210 of about 1,925,096 (328)
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation [PDF]
Jianxu Chen +4 more
openalex +1 more source
This study develops a deep learning‐based pathomics model to predict survival outcomes in pancreatic cancer patients. The CrossFormer architecture analyzes routine H&E‐stained tissue slides, identifying key prognostic features including stromal patterns, cellular characteristics, and immune infiltration.
Qiangda Chen +22 more
wiley +1 more source
Automated assessment of small bowel and colon cleansing in enteroscopy using a convolutional neural network. [PDF]
Marílio Cardoso P +11 more
europepmc +1 more source
Acoustic holograms act as key tools for information encryption, yet current schemes limit encryption dimensionality/security and require time‐consuming decryption. A compact device integrates multi‐dimensional cascaded acoustic holography with particle manipulation, employs extra secret keys, enables rapid decryption, and is validated via 1D/2D/3D ...
Qin Lin +8 more
wiley +1 more source
Architecture Design of a Convolutional Neural Network Accelerator for Heterogeneous Computing Based on a Fused Systolic Array. [PDF]
Zong Y, Ma Z, Ren J, Cao Y, Li M, Liu B.
europepmc +1 more source
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Fabio Pardo +2 more
openalex +2 more sources
EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing [PDF]
Shunzhi Yang +5 more
openalex +1 more source
S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu +6 more
wiley +1 more source
Antimicrobial peptide prediction based on contrastive learning and gated convolutional neural network. [PDF]
Li G, Wang L, Luo J, Liang C.
europepmc +1 more source

