Titlebook: Artificial Intelligence in Medicine; 19th International C Allan Tucker,Pedro Henriques Abreu,David Riaño Conference proceedings 2021 The Ed

Ventilator

Primary Care Datasets for Early Lung Cancer Detection: An AI Led Approacharch focuses initially on lung cancer but can be extended to other types of cancer. Additional challenges are present in this type of data due to the irregular and infrequent nature of doing pathology tests, which are also considered in designing the AI solution. Our findings demonstrate that hemato

ABHOR

Addressing Extreme Imbalance for Detecting Medications Mentioned in Twitter User Timelineson, a classifier based on a lexicon and a BERT-base neural network achieved a 0.838 F1-score, a score similar to the score achieved by the best classifier on this task during the #SMM4H’20 competition, but it processed the corpus 28 times faster - a positive result, since processing speed is still a

仇恨

thwart

Crepitus

Glossy

Analysis of Health Screening Records Using Interpretations of Predictive Modelsd that the model makes good predictions using a number of attributes conventionally known to be related to diabetes, but also those not commonly used in the diagnosis of diabetes. A sensitivity analysis showed that the predictions’ changes were mostly consistent with our intuition on how daily behav

Pepsin

phlegm

A Petri Dish for Histopathology Image Analysiss the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists. However, challenges such as limited data, costly annotation, and processing high-resolution and variable-size images make it difficult to quickly iterate over model designs..Throughou

半身雕像

fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiserith any widely used imaging modality, there is a need to ensure the quality of the same, with missing values being highly frequent due to the presence of artifacts or sub-optimal imaging resolutions. Our work focus on missing values imputation on multivariate signal data. To do so, a new imputation

Offbeat

Bayesian Deep Active Learning for Medical Image Analysisequate performance. However, such labelled images are costly to acquire in time, labour, and human expertise. We propose a novel practical Bayesian Active Learning approach using Dropweights and overall bias-corrected uncertainty measure to suggest which unlabelled image to annotate. Experiments wer