scholarly journals A Critical Review on Machine Learning based Liver Tumor Classification

2022 ◽  
Vol 11 (1) ◽  
pp. 73-94
Author(s):  
Munipraveena Rela ◽  
Suryakari Nagaraja Rao ◽  
P. Ramana Reddy
Keyword(s):  
Machine Learning ◽  
Liver Tumor ◽  
Critical Review ◽  
Tumor Classification

PH-0265 Diaphragm motion prediction based on optical surface with machine learning for liver tumor SBRT

2021 ◽  
Vol 161 ◽  
pp. S173-S174
Author(s):  
Z. Dai ◽  
Y. Zhang ◽  
Q. He ◽  
S. Zhao ◽  
Y. Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Tumor ◽  
Motion Prediction ◽  
Optical Surface ◽  
Diaphragm Motion

Paradigms of Machine Learning and Data Analytics

2020 ◽  
pp. 156-174
Author(s):  
Pawan Kumar Chaurasia
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Deep Learning ◽  
Critical Review ◽  
Data Analytics ◽  
Disease Patient ◽  
Learning Tools ◽  
Heart Disease Patient ◽  
Tools And Techniques ◽  
New Hypothesis

This chapter conducts a critical review on ML and deep learning tools and techniques in the field of heart disease related to heart disease complexity, prediction, and diagnosis. Only specific papers are selected for the study to extract useful information, which stimulated a new hypothesis to understand further investigation of the heart disease patient.

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation

2019 ◽  
Author(s):  
Castrense Savojardo ◽  
Pier Luigi Martelli ◽  
Rita Casadio ◽  
Piero Fariselli
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Protein Stability ◽  
Critical Review ◽  
Energy Difference ◽  
General View ◽  
Free Energy Difference ◽  
Protein Variation

Abstract A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A- > B) and its reverse (B- > A) must have the opposite value of the free energy difference (ΔΔGAB = − ΔΔGBA). In this letter, we complement the Fang’s paper presenting a more general view of the problem. In particular, a machine learning-based method, published in 2015 (INPS), addressed the bias issue directly. We include the analysis of the missing method, showing that INPS is nearly insensitive to the addressed problem.

scholarly journals Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

2020 ◽  
Vol 22 (4) ◽  
pp. 334-355 ◽  
Author(s):  
Laurie‐Anne Claude ◽  
Josselin Houenou ◽  
Edouard Duchesnay ◽  
Pauline Favre
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Critical Review
Sign in / Sign up

Export Citation Format

Share Document