AIMC Topic: Humans

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Interpreting a recurrent neural network's predictions of ICU mortality risk.

Journal of biomedical informatics
Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions. Algorithms such as Recurrent Neural Networks (RNNs) when applied to Ele...

[Machine learning is unlikely to improve the diagnostic performance of medical history taking].

Nederlands tijdschrift voor geneeskunde
Use of machine learning has been proposedtoimprovethediagnostic performance of medicalhistorytaking, whichwould first have tobestandardized. Thiscommentary reviews theoretical, practical andethicalconsiderationswithregardtothisproposal.

Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study.

Chinese medical journal
BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, ad...

Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition.

Sensors (Basel, Switzerland)
The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion a...

Physician-Friendly Tool Center Point Calibration Method for Robot-Assisted Puncture Surgery.

Sensors (Basel, Switzerland)
After each robot end tool replacement, tool center point (TCP) calibration must be performed to achieve precise control of the end tool. This process is also essential for robot-assisted puncture surgery. The purpose of this article is to solve the p...

An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.

Genome biology
BACKGROUND: Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transc...

Universal adversarial attacks on deep neural networks for medical image classification.

BMC medical imaging
BACKGROUND: Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN tasks against adversarial attacks, as high-...

Predicting Age From Optical Coherence Tomography Scans With Deep Learning.

Translational vision science & technology
PURPOSE: To assess whether age can be predicted from deep learning analysis of peripapillary spectral-domain optical coherence tomography (SD-OCT) B-scans and to determine the importance of specific retinal areas on the predictions.

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on...

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.

PLoS computational biology
The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for ...