AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This study introduces a novel 3D registration framework centered on a dynamic wavelet transform module that achieves superior registration fidelity, highlighting its potential for practical clinical implementation.
This report introduces a new unsupervised deformable 3D registration network called AWaRe Net developed for the purpose of overcoming the drawbacks of existing techniques with regard to multi scale fusion and losing key information. AWaRe Net replaces fixed Haar wavelets with the ability to create adaptive wavelet filt...
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Abstract. Three-dimensional (3D) brain image registration is a vital branch of medical image processing, with broad applications in disease monitoring, preoperative planning, and multimodal fusion. The current registration models based on the U-Net framework are limited by the implicit feature learning paradigm between...
Existing object detection models often face challenges in medical imaging, including limited feature fusion capabilities, constrained receptive fields, and the prevalent issue of class imbalance. To address these limitations, this paper proposes FDANet (Fine-Grained Detail Aggregation Network), a novel multi-module fea...
Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment stages, and training-objective additions such as regularization losses. Papers tend to adopt both at once, so it is unclear which is doing the work. I ran a controlled abla...
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