AIMC Topic: Deep Learning

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Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great cha...

Weakly supervised deep learning for determining the prognostic value of F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type.

European journal of nuclear medicine and molecular imaging
PURPOSE: To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment F-FDG PET/CT resul...

Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosi...

Deep learning based digital cell profiles for risk stratification of urine cytology images.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Urine cytology is a test for the detection of high-grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells...

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Sensors (Basel, Switzerland)
Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is r...

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning.

Sensors (Basel, Switzerland)
In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods pr...

A hierarchical three-step superpixels and deep learning framework for skin lesion classification.

Methods (San Diego, Calif.)
Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma is one of the most dangerous skin cancer types has a high mortality rate. An estimated 196,060 melanoma cases will be diagnosed in 2020 in the USA. M...

Translating amyloid PET of different radiotracers by a deep generative model for interchangeability.

NeuroImage
It is challenging to compare amyloid PET images obtained with different radiotracers. Here, we introduce a new approach to improve the interchangeability of amyloid PET acquired with different radiotracers through image-level translation. Deep genera...

Detecting white spot lesions on dental photography using deep learning: A pilot study.

Journal of dentistry
OBJECTIVES: We aimed to apply deep learning to detect white spot lesions in dental photographs.