AIMC Topic: Deep Learning

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BABEL: using deep learning to translate between single-cell datasets.

Communications biology
Recent advances in sequencing and barcoding technologies have enabled researchers to simultaneously profile gene expression, chromatin accessibility, and/or protein levels in single cells. However, these multiomic techniques often pose technical and ...

Learning deep features for dead and living breast cancer cell classification without staining.

Scientific reports
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer ce...

Predicting sex from retinal fundus photographs using automated deep learning.

Scientific reports
Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reporte...

A review of deep learning based methods for medical image multi-organ segmentation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical ap...

Deep learning applied to electroencephalogram data in mental disorders: A systematic review.

Biological psychology
In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictiv...

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks.

PLoS computational biology
Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes...

A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.

PloS one
Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achie...

A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens.

Cancer cytopathology
BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, their application to practical assistance systems has not been achieved. In addition, whether deep learning systems (DLSs) can perform diagnoses that ca...

Deep learning for predicting COVID-19 malignant progression.

Medical image analysis
As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first as...

Deep learning: step forward to high-resolution in vivo shortwave infrared imaging.

Journal of biophotonics
Shortwave infrared window (SWIR: 1000-1700 nm) represents a major improvement compared to the NIR-I region (700-900 nm) in terms of temporal and spatial resolutions in depths down to 4 mm. SWIR is a fast and cheap alternative to more precise methods ...