AIMC Topic: Image Processing, Computer-Assisted

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CycleGAN-based deep learning technique for artifact reduction in fundus photography.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: A low quality of fundus photograph with artifacts may lead to false diagnosis. Recently, a cycle-consistent generative adversarial network (CycleGAN) has been introduced as a tool to generate images without matching paired images. Therefore,...

Working memory load-dependent changes in cortical network connectivity estimated by machine learning.

NeuroImage
Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to cha...

SPST-CNN: Spatial pyramid based searching and tagging of liver's intraoperative live views via CNN for minimal invasive surgery.

Journal of biomedical informatics
Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility. While image guidance has the potential to address this barrier, intraoperative facto...

Library of deep-learning image segmentation and outcomes model-implementations.

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)
An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of mod...

Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences.

Neural networks : the official journal of the International Neural Network Society
Due to their unprecedented capacity to learn patterns from raw data, deep neural networks have become the de facto modeling choice to address complex machine learning tasks. However, recent works have emphasized the vulnerability of deep neural netwo...

Deep complex convolutional network for fast reconstruction of 3D late gadolinium enhancement cardiac MRI.

NMR in biomedicine
Several deep-learning models have been proposed to shorten MRI scan time. Prior deep-learning models that utilize real-valued kernels have limited capability to learn rich representations of complex MRI data. In this work, we utilize a complex-valued...

AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT.

Medical physics
PURPOSE: Sparse-data computed tomography (CT) frequently occurs, such as breast tomosynthesis, C-arm CT, on-board four-dimensional cone-beam CT (4D CBCT), and industrial CT. However, sparse-data image reconstruction remains challenging due to highly ...

Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces.

TheScientificWorldJournal
Age and gender predictions of unfiltered faces classify unconstrained real-world facial images into predefined age and gender. Significant improvements have been made in this research area due to its usefulness in intelligent real-world applications....

Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy.

Medical image analysis
In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to fully automatic segmentation of Glioblastoma brain tumors with high- and low-grade. The proposed CNNs model is inspired by the Occipito-Temporal pathway which has ...