AIMC Topic: Image Processing, Computer-Assisted

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Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network.

Medical & biological engineering & computing
The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processin...

Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection.

IEEE transactions on medical imaging
Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduri...

A gentle introduction to deep learning in medical image processing.

Zeitschrift fur medizinische Physik
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakt...

Using deep-learning algorithms to derive basic characteristics of social media users: The Brexit campaign as a case study.

PloS one
A recurrent criticism concerning the use of online social media data in political science research is the lack of demographic information about social media users. By employing a face-recognition algorithm to the profile pictures of Facebook users, t...

Label-free classification of neurons and glia in neural stem cell cultures using a hyperspectral imaging microscopy combined with machine learning.

Scientific reports
Due to a growing demand for a viable label-free observation method in the biomedical field, many techniques, such as quantitative phase imaging and Raman spectroscopy, have been studied, and a complementary approach, hyperspectral imaging, has also b...

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images.

Biomedical engineering online
BACKGROUND: Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically...

Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation.

IEEE transactions on pattern analysis and machine intelligence
We investigate two crucial and closely-related aspects of CNNs for optical flow estimation: models and training. First, we design a compact but effective CNN model, called PWC-Net, according to simple and well-established principles: pyramidal proces...

Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.

NeuroImage. Clinical
We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detecti...

Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images.

IEEE transactions on bio-medical engineering
An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT ex...