Machine learning to detect signatures of disease in liquid biopsies–a user's guide

J Ko, SN Baldassano, PL Loh, K Kording, B Litt… - Lab on a Chip, 2018‏ - pubs.rsc.org
New technologies that measure sparse molecular biomarkers from easily accessible bodily
fluids (eg blood, urine, and saliva) are revolutionizing disease diagnostics and precision …

[HTML][HTML] Machine learning of Raman spectroscopy data for classifying cancers: a review of the recent literature

N Blake, R Gaifulina, LD Griffin, IM Bell, GMH Thomas - Diagnostics, 2022‏ - mdpi.com
Raman Spectroscopy has long been anticipated to augment clinical decision making, such
as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far …

Toward a perspectivist turn in ground truthing for predictive computing

F Cabitza, A Campagner, V Basile - … of the AAAI Conference on Artificial …, 2023‏ - ojs.aaai.org
Abstract Most current Artificial Intelligence applications are based on supervised Machine
Learning (ML), which ultimately grounds on data annotated by small teams of experts or …

Unintended consequences of machine learning in medicine

F Cabitza, R Rasoini, GF Gensini - Jama, 2017‏ - jamanetwork.com
Overthepastdecade, machinelearningtechniqueshave made substantial advances in many
domains. In health care, global interest in the potential of machine learning hasincreased; …

The impact of inconsistent human annotations on AI driven clinical decision making

A Sylolypavan, D Sleeman, H Wu, M Sim - NPJ Digital Medicine, 2023‏ - nature.com
In supervised learning model development, domain experts are often used to provide the
class labels (annotations). Annotation inconsistencies commonly occur when even highly …

Cancer diagnosis through IsomiR expression with machine learning method

Z Liao, D Li, X Wang, L Li, Q Zou - Current Bioinformatics, 2018‏ - benthamdirect.com
Background: IsomiR is an isoform of microRNA (miRNA), and its sequences vary from those
of a reference miRNA, which arose with the advencements of deep sequencing, high miRNA …

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

G Di Sario, V Rossella, ES Famulari, A Maurizio… - Frontiers in …, 2023‏ - frontiersin.org
In the last years, liquid biopsy gained increasing clinical relevance for detecting and
monitoring several cancer types, being minimally invasive, highly informative and replicable …

Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

T Saba, SU Khan, N Islam, N Abbas… - Microscopy research …, 2019‏ - Wiley Online Library
The advancement of computer‐and internet‐based technologies has transformed the nature
of services in healthcare by using mobile devices in conjunction with cloud computing. The …

Deep learning of circulating tumour cells

LL Zeune, YE Boink, G van Dalum, A Nanou… - Nature Machine …, 2020‏ - nature.com
Circulating tumour cells (CTCs) found in the blood of cancer patients are a promising
biomarker in precision medicine. However, their use is currently hindered by their low …

Challenges for machine learning in clinical translation of big data imaging studies

NK Dinsdale, E Bluemke, V Sundaresan, M Jenkinson… - Neuron, 2022‏ - cell.com
Combining deep learning image analysis methods and large-scale imaging datasets offers
many opportunities to neuroscience imaging and epidemiology. However, despite these …