AIMC Topic: Deep Learning

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Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.

Scientific reports
Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of dr...

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning.

Journal of medical systems
The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classif...

Determination of output factor for CyberKnife using scintillation dosimetry and deep learning.

Physics in medicine and biology
. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnife. The objective of this work is to demonstrate the use of a plastic scintillator imaged with a commercial cam...

Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairment.

PloS one
Mild Cognitive Impairment (MCI) is a condition characterized by a decline in cognitive abilities, specifically in memory, language, and attention, that is beyond what is expected due to normal aging. Detection of MCI is crucial for providing appropri...

Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study.

Magnetic resonance in medicine
PURPOSE: To mitigate inhomogeneity at 7T for multi-channel transmit arrays using unsupervised deep learning with convolutional neural networks (CNNs).

Analysis of static plantar pressure data with capsule networks: Diagnosing ataxia in MS patients with a deep learning-based approach.

Multiple sclerosis and related disorders
In this study, it was aimed to detect ataxia in patients with Multiple Sclerosis (MS) by utilizing static plantar pressure data and capsule networks (CapsNet), one of the deep learning (DL) architectures. CapsNet is also equipped with a robust dynami...

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

International journal of medical informatics
OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL).

Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.

British journal of cancer
BACKGROUND: Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk st...

An integrated passive acoustic monitoring and deep learning pipeline for black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar.

American journal of primatology
The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine m...