AIMC Topic: Neural Networks, Computer

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Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.

Scientific reports
Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep...

Brain tumor image segmentation method using hybrid attention module and improved mask RCNN.

Scientific reports
To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional neural network. By incorporating squeeze-and-excitati...

Photoacoustic Quantification of Tissue Oxygenation Using Conditional Invertible Neural Networks.

IEEE transactions on medical imaging
Intelligent systems in interventional healthcare depend on the reliable perception of the environment. In this context, photoacoustic tomography (PAT) has emerged as a non-invasive, functional imaging modality with great clinical potential. Current r...

Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.

Future medicinal chemistry
Immune checkpoint inhibitors targeting PD-L1 are crucial in cancer research for preventing cancer cells from evading the immune system. This study developed a screening model combining ANN, molecular similarity, and GNINA 1.0 docking to target PD-L1...

Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effect...

Time-optimal open-loop set stabilization of Boolean control networks.

Neural networks : the official journal of the International Neural Network Society
We show that for stabilization of Boolean control networks (BCNs) with unobservable initial states, open-loop control and close-loop control are not equivalent. An example is given to illustrate the nonequivalence. Enlightened by the nonequivalence, ...

Towards a configurable and non-hierarchical search space for NAS.

Neural networks : the official journal of the International Neural Network Society
Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can ...

Sample selection of adversarial attacks against traffic signs.

Neural networks : the official journal of the International Neural Network Society
In the real world, the correct recognition of traffic signs plays a crucial role in vehicle autonomous driving and traffic monitoring. The research on its adversarial attack can test the security of vehicle autonomous driving system and provide enlig...

State transition learning with limited data for safe control of switched nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
Switching dynamics are prevalent in real-world systems, arising from either intrinsic changes or responses to external influences, which can be appropriately modeled by switched systems. Control synthesis for switched systems, especially integrating ...

Learning clustering-friendly representations via partial information discrimination and cross-level interaction.

Neural networks : the official journal of the International Neural Network Society
Despite significant advances in the deep clustering research, there remain three critical limitations to most of the existing approaches. First, they often derive the clustering result by associating some distribution-based loss to specific network l...