AIMC Topic: Algorithms

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DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The use of several stains during histology sample preparation can be useful for fusing complementary information about different tissue structures. It reveals distinct tissue properties that combined may be useful for gradin...

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra.

Journal of dairy science
Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an inter...

Implementation of convolutional neural network approach for COVID-19 disease detection.

Physiological genomics
In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a gi...

Compressing Deep Networks by Neuron Agglomerative Clustering.

Sensors (Basel, Switzerland)
In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large stora...

Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Speech emotion recognition (SER) plays a significant role in human-machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotio...

A permutation method for network assembly.

PloS one
We present a method for assembling directed networks given a prescribed bi-degree (in- and out-degree) sequence. This method utilises permutations of initial adjacency matrix assemblies that conform to the prescribed in-degree sequence, yet violate t...

Tumor segmentation in automated whole breast ultrasound using bidirectional LSTM neural network and attention mechanism.

Ultrasonics
Accurate breast mass segmentation of automated breast ultrasound (ABUS) is a great help to breast cancer diagnosis and treatment. However, the lack of clear boundary and significant variation in mass shapes make the automatic segmentation very challe...

Unsupervised feature learning for self-tuning neural networks.

Neural networks : the official journal of the International Neural Network Society
In recent years transfer learning has attracted much attention due to its ability to adapt a well-trained model from one domain to another. Fine-tuning is one of the most widely-used methods which exploit a small set of labeled data in the target dom...

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.

Human movement science
There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable se...

Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification.

Sensors (Basel, Switzerland)
Observation of neuromotor development at an early stage of an infant's life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the...