Advancements in Clinical Evaluation and Regulatory Frameworks for AI-Driven Software as a Medical Device (SaMD).

Owing to the rapid progress in artificial intelligence (AI) and the widespread use of generative learning, the problem of sparse data has been solved effectively in various research fields. The application of AI technologies has resulted in important transformations in healthcare, particularly in radiology. To ensure the high quality, safety, and effectiveness of AI and machine learning (ML) medical devices, the US Food and Drug Administration (FDA) has established regulatory guidelines to support the performance evaluation of medical devices.

Latent Semantic and Disentangled Attention.

Sequential learning using transformer has achieved state-of-the-art performance in natural language tasks and many others. The key to this success is the multi-head self attention which encodes and gathers the features from individual tokens of an input sequence. The mapping or decoding is performed to produce an output sequence via cross attention.

Hyperspectral signature-band extraction and learning: an example of sugar content prediction of Syzygium samarangense.

This study proposes a method to extract the signature bands from the deep learning models of multispectral data converted from the hyperspectral data. The signature bands with two deep-learning models were further used to predict the sugar content of the Syzygium samarangense. Firstly, the hyperspectral data with the bandwidths lower than 2.

Learning Flow-Based Disentanglement.

Face reenactment aims to generate the talking face images of a target person given by a face image of source person. It is crucial to learn latent disentanglement to tackle such a challenging task through domain mapping between source and target images. The attributes or talking features due to domains or conditions become adjustable to generate target images from source images.

Learning Continuous-Time Dynamics With Attention.

Learning the hidden dynamics from sequence data is crucial. Attention mechanism can be introduced to spotlight on the region of interest for sequential learning. Traditional attention was measured between a query and a sequence based on a discrete-time state trajectory.

Sugariness prediction of Syzygium samarangense using convolutional learning of hyperspectral images.

Sugariness is one of the most important indicators to measure the quality of Syzygium samarangense, which is also known as the wax apple. In general, farmers used to measure sugariness by testing the extracted juice of the wax apple products. Such a destructive way to measure sugariness is not only labor-consuming but also wasting products.

Hierarchical and Self-Attended Sequence Autoencoder.

It is important and challenging to infer stochastic latent semantics for natural language applications. The difficulty in stochastic sequential learning is caused by the posterior collapse in variational inference. The input sequence is disregarded in the estimated latent variables.

Contrastive Adversarial Domain Adaptation Networks for Speaker Recognition.

Domain adaptation aims to reduce the mismatch between the source and target domains. A domain adversarial network (DAN) has been recently proposed to incorporate adversarial learning into deep neural networks to create a domain-invariant space. However, DAN's major drawback is that it is difficult to find the domain-invariant space by using a single feature extractor.

Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography.

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI.

Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation.

Recent advances in imaging and biotechnology have tremendously improved the availability of quantitative imaging (radiomics) and molecular data (radiogenomics) for radiotherapy patients. This big data development with its comprehensive nature promises to transform outcome modeling in radiotherapy from few dose-volume metrics into utilizing more data-driven analytics. However, it also presents new profound challenges and creates new tasks for alleviating uncertainties arising from dealing with heterogeneous data and complex big data analytics.

Deep reinforcement learning for automated radiation adaptation in lung cancer.

Purpose: To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2).

Methods: In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables.

Tensor-Factorized Neural Networks.

The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations.

Deep Unfolding for Topic Models.

Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification.

Bayesian Recurrent Neural Network for Language Modeling.

A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer.

Hierarchical Theme and Topic Modeling.

Considering the hierarchical data groupings in text corpus, e.g., words, sentences, and documents, we conduct the structural learning and infer the latent themes and topics for sentences and words from a collection of documents, respectively.

Nonstationary source separation using sequential and variational Bayesian learning.

Independent component analysis (ICA) is a popular approach for blind source separation where the mixing process is assumed to be unchanged with a fixed set of stationary source signals. However, the mixing system and source signals are nonstationary in real-world applications, e.g.

Maximum confidence hidden markov modeling for face recognition.

This paper presents a hybrid framework of feature extraction and hidden Markov modeling(HMM) for two-dimensional pattern recognition. Importantly, we explore a new discriminative training criterion to assure model compactness and discriminability. This criterion is derived from hypothesis test theory via maximizing the confidence of accepting the hypothesis that observations are from target HMM states rather than competing HMM states.