AIMC Topic: Deep Learning

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Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.

Computers in biology and medicine
Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate and early prediction of various CVDs from the electrocardiogram (ECG) is vital for the prevention of...

OCDet: A comprehensive ovarian cell detection model with channel attention on immunohistochemical and morphological pathology images.

Computers in biology and medicine
BACKGROUND: Ovarian cancer is among the most lethal gynecologic malignancy that threatens women's lives. Pathological diagnosis is a key tool for early detection and diagnosis of ovarian cancer, guiding treatment strategies. The evaluation of various...

Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convoluti...

Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.

Scientific reports
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both M...

Risk score stratification of cutaneous melanoma patients based on whole slide images analysis by deep learning.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited mo...

Combining various training and adaptation algorithms for ensemble few-shot classification.

Neural networks : the official journal of the International Neural Network Society
To mitigate the shortage of labeled data, Few-Shot Classification (FSC) methods train deep neural networks (DNNs) on a base dataset with sufficient labeled data, and then adapt them to target tasks using a few labeled data. Despite notable progress, ...

FDuDoCLNet: Fully dual-domain contrastive learning network for parallel MRI reconstruction.

Magnetic resonance imaging
Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that is widely used for high-resolution imaging of soft tissues and organs. However, the slow speed of MRI imaging, especially in high-resolution or dynamic scans, makes MRI...

Recipes and ingredients for deep learning models of 3D genome folding.

Current opinion in genetics & development
Three-dimensional genome folding plays roles in gene regulation and disease. In this review, we compare and contrast recent deep learning models for predicting genome contact maps. We survey preprocessing, architecture, training, evaluation, and inte...

Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography.

Neurologia medico-chirurgica
Non-enhanced head computed tomography is widely used for patients presenting with head trauma or stroke, given acute intracranial hemorrhage significantly influences clinical decision-making. This study aimed to develop a deep learning algorithm, ref...

Research of orthodontic soft tissue profile prediction based on conditional generative adversarial networks.

Journal of dentistry
OBJECTIVE: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.