AIMC Topic: Deep Learning

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Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization.

Scientific reports
The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnan...

Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer.

BMC cancer
BACKGROUND: Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy develo...

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning.

Computational intelligence and neuroscience
Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. Howeve...

Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND: Significant interobserver and interstudy variability occurs for left ventricular (LV) functional indices despite standardization of measurement techniques. Artificial intelligence models trained on adult echocardiograms are not likely to ...

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on...

Multimodal Sensor-Input Architecture with Deep Learning for Audio-Visual Speech Recognition in Wild.

Sensors (Basel, Switzerland)
This paper investigates multimodal sensor architectures with deep learning for audio-visual speech recognition, focusing on in-the-wild scenarios. The term "in the wild" is used to describe AVSR for unconstrained natural-language audio streams and vi...

A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images.

Biosensors
Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called micr...

Deep learning approach to detection of colonoscopic information from unstructured reports.

BMC medical informatics and decision making
BACKGROUND: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information em...

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO.

Scientific reports
Congenital Generalized Lipodystrophy (CGL) is a rare autosomal recessive disease characterized by near complete absence of functional adipose tissue from birth. CGL diagnosis can be based on clinical data including acromegaloid features, acanthosis n...

A deep learning model for detection of leukocytes under various interference factors.

Scientific reports
The accurate detection of leukocytes is the basis for the diagnosis of blood system diseases. However, diagnosing leukocyte disorders by doctors is time-consuming and requires extensive experience. Automated detection methods with high accuracy can i...