AIMC Topic: Image Processing, Computer-Assisted

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diploS/HIC: An Updated Approach to Classifying Selective Sweeps.

G3 (Bethesda, Md.)
Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective s...

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

Experimental brain research
Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in ...

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BMC cancer
BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. Howev...

Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images.

IEEE/ACM transactions on computational biology and bioinformatics
Likely drug candidates which are identified in traditional pre-clinical drug screens often fail in patient trials, increasing the societal burden of drug discovery. A major contributing factor to this phenomenon is the failure of traditional in vitro...

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new appro...

Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images.

International journal of computer assisted radiology and surgery
PURPOSE: Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular dise...

An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine.

Computational intelligence and neuroscience
Multispectral palmprint recognition system (MPRS) is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE) and regularized extrem...

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

Journal of psychiatric research
Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well es...

Large Scale Image Segmentation with Structured Loss Based Deep Learning for Connectome Reconstruction.

IEEE transactions on pattern analysis and machine intelligence
We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-Net, t...

Medical Image Fusion Based on Sparse Representation and PCNN in NSCT Domain.

Computational and mathematical methods in medicine
The clinical assistant diagnosis has a high requirement for the visual effect of medical images. However, the low frequency subband coefficients obtained by the NSCT decomposition are not sparse, which is not conducive to maintaining the details of t...