AIMC Topic: Neural Networks, Computer

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Phase Contrast Image Restoration by Formulating Its Imaging Principle and Reversing the Formulation With Deep Neural Networks.

IEEE transactions on medical imaging
Phase contrast microscopy, as a noninvasive imaging technique, has been widely used to monitor the behavior of transparent cells without staining or altering them. Due to the optical principle of the specifically-designed microscope, phase contrast m...

FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network.

IEEE transactions on medical imaging
An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work...

Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework.

IEEE transactions on medical imaging
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset o...

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion.

IEEE transactions on medical imaging
Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifacts, denoising is largely studied both within the medical imaging community and beyond the community as a gener...

Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification.

Arquivos brasileiros de oftalmologia
PURPOSE: This study aimed to evaluate the classification performance of pretrained convolutional neural network models or architectures using fundus image dataset containing eight disease labels.

Graph Transformer for Drug Response Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of...

Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation Extraction.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automatically design the optimal neural architectures for many problems such as node classification and graph classification. In the existing GNAS frameworks,...

Self-Attention Based Neural Network for Predicting RNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics
Proteins binding to Ribonucleic Acid (RNA) inside cells are called RNA-binding proteins (RBP), which play a crucial role in gene regulation. The identification of RNA-protein binding sites helps to understand the function of RBP better. Although many...

Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation.

IEEE/ACM transactions on computational biology and bioinformatics
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein str...

PiTLiD: Identification of Plant Disease From Leaf Images Based on Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural networ...