AIMC Topic: Reproducibility of Results

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What Does the Machine Learn? Knowledge Representations of Chemical Reactivity.

Journal of chemical information and modeling
In a departure from conventional chemical approaches, data-driven models of chemical reactions have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not prov...

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...

Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.

Physics in medicine and biology
Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission tomography (PET) remains challenging. Common problems include truncation, inter-scan motion, and erroneous transformation of structural voxel-intensitie...

A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system.

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)
PURPOSE: EPID dosimetry in the Unity MR-Linac system allows for reconstruction of absolute dose distributions within the patient geometry. Dose reconstruction is accurate for the parts of the beam arriving at the EPID through the MRI central unattenu...

An AI-based approach in determining the effect of meteorological factors on incidence of malaria.

Frontiers in bioscience (Landmark edition)
This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers i...

Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations.

Gastroenterology
BACKGROUND & AIMS: Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps b...

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy.

Medical image analysis
There are two challenges associated with the interpretability of deep learning models in medical image analysis applications that need to be addressed: confidence calibration and classification uncertainty. Confidence calibration associates the class...

Calculating the target exposure index using a deep convolutional neural network and a rule base.

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)
PURPOSE: The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnos...

A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.

Asian journal of psychiatry
BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy...

The application of unsupervised deep learning in predictive models using electronic health records.

BMC medical research methodology
BACKGROUND: The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder fea...