AIMC Topic: Probability

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Human-machine cooperation meta-model for clinical diagnosis by adaptation to human expert's diagnostic characteristics.

Scientific reports
Artificial intelligence (AI) using deep learning approaches the capabilities of human experts in medical image diagnosis. However, due to liability issues in medical decisions, AI is often relegated to an assistant role. Based on this responsibility ...

Mean-field neural networks: Learning mappings on Wasserstein space.

Neural networks : the official journal of the International Neural Network Society
We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e.g. in mean-field games/control problems. Two classes of neural networks based on bin density a...

Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks have become increasingly significant in our daily lives due to their remarkable performance. The issue of adversarial examples, which are responsible for the vulnerability problem of deep neural networks, has attracted the attent...

Oral Cancer Prediction Using a Probability Neural Network (PNN).

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: In India, usually, oral cancer is mostly identified at a progressive stage of malignancy. Hence, we are motivated to identify oral cancer in its early stages, which helps to increase the lifetime of the patient, but this early detection is...

Metaparametric Neural Networks for Survival Analysis.

IEEE transactions on neural networks and learning systems
Survival analysis is a critical tool for the modeling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex machinery. However, current neural network models provide an imperfect solutio...

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.

ACS synthetic biology
Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete inform...

External validation of machine learning algorithm predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients using a Taiwanese cohort.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND/PURPOSE: Identifying patients at risk of prolonged opioid use after surgery prompts appropriate prescription and personalized treatment plans. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was developed to pred...

Multi-Constraint Latent Representation Learning for Prognosis Analysis Using Multi-Modal Data.

IEEE transactions on neural networks and learning systems
The Cox proportional hazard model has been widely applied to cancer prognosis prediction. Nowadays, multi-modal data, such as histopathological images and gene data, have advanced this field by providing histologic phenotype and genotype information....

Spatial-Spectral Unified Adaptive Probability Graph Convolutional Networks for Hyperspectral Image Classification.

IEEE transactions on neural networks and learning systems
In hyperspectral image (HSI) classification task, semisupervised graph convolutional network (GCN)-based methods have received increasing attention. However, two problems still need to be addressed. The first is that the initial graph structure in th...

Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data.

Journal of neurophysiology
Unsupervised, data-driven methods are commonly used in neuroscience to automatically decompose data into interpretable patterns. These patterns differ from one another depending on the assumptions of the models. How these assumptions affect specific ...