AIMC Topic: Neural Networks, Computer

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Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables.

Journal of chemical information and modeling
Retrosynthesis is an essential task in organic chemistry for identifying the synthesis pathways of newly discovered materials, and with the recent advances in deep learning, there have been growing attempts to solve the retrosynthesis problem through...

Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network.

Sensors (Basel, Switzerland)
Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the diagnosis and prognosis of diseases. In this study, we propose a deep learning-based method to segme...

Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition.

Sensors (Basel, Switzerland)
The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion a...

An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.

Genome biology
BACKGROUND: Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transc...

Universal adversarial attacks on deep neural networks for medical image classification.

BMC medical imaging
BACKGROUND: Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN tasks against adversarial attacks, as high-...

A transfer learning model with multi-source domains for biomedical event trigger extraction.

BMC genomics
BACKGROUND: Automatic extraction of biomedical events from literature, that allows for faster update of the latest discoveries automatically, is a heated research topic now. Trigger word recognition is a critical step in the process of event extracti...

Predicting Age From Optical Coherence Tomography Scans With Deep Learning.

Translational vision science & technology
PURPOSE: To assess whether age can be predicted from deep learning analysis of peripapillary spectral-domain optical coherence tomography (SD-OCT) B-scans and to determine the importance of specific retinal areas on the predictions.

Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss.

Computational intelligence and neuroscience
Semantic classification of Chinese long discourses is an important and challenging task. Discourse text is high-dimensional and sparse. Furthermore, when the number of classes of dataset is large, the data distribution will be seriously imbalanced. I...

A novel cross-validation strategy for artificial neural networks using distributed-lag environmental factors.

PloS one
In recent years, machine learning methods have been applied to various prediction scenarios in time-series data. However, some processing procedures such as cross-validation (CV) that rearrange the order of the longitudinal data might ruin the serial...

Lung cancer histology classification from CT images based on radiomics and deep learning models.

Medical & biological engineering & computing
Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histolog...