AIMC Topic: Neural Networks, Computer

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P-DIFF+: Improving learning classifier with noisy labels by Noisy Negative Learning loss.

Neural networks : the official journal of the International Neural Network Society
Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called...

Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.

Radiological physics and technology
In brain magnetic resonance imaging (MRI) examinations, rapidly acquired two-dimensional (2D) T1-weighted sagittal slices are typically used to confirm brainstem atrophy and the presence of signals in the posterior pituitary gland. Image segmentation...

MedQ: Lossless ultra-low-bit neural network quantization for medical image segmentation.

Medical image analysis
Implementing deep convolutional neural networks (CNNs) with boolean arithmetic is ideal for eliminating the notoriously high computational expense of deep learning models. However, although lossless model compression via weight-only quantization has ...

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens.

Analytical chemistry
The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, w...

Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis.

JAMA network open
IMPORTANCE: A high level of surgical skill is essential to prevent intraoperative problems. One important aspect of surgical education is surgical skill assessment, with pertinent feedback facilitating efficient skill acquisition by novices.

A stacking ensemble deep learning approach to cancer type classification based on TCGA data.

Scientific reports
Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers...

Interpreting wide-band neural activity using convolutional neural networks.

eLife
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the repres...

A Survey of Soft Computing Approaches in Biomedical Imaging.

Journal of healthcare engineering
Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clinics. Soft computing plays a major role in the recent advances in medical imaging. It handles uncertainties and improves the qualities of an image. Unt...

Machine learning prediction of dropping out of outpatients with alcohol use disorders.

PloS one
BACKGROUND: Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probabili...