SVM in Classification of stage 0~II and III~IV with Breast Cancer : A Retrospective Cohort Study on a bicentric cohort DOI
Yeang Guo,

Tao Tan,

Ronglin Ronglin

et al.

Published: Aug. 25, 2023

Objective: The objective is to develop a predictive model utilizing Support Vector Machines (SVM) for the purpose of classifying clinical stage breast cancer.

Language: Английский

Optimizing BI-RADS 4 Lesion Assessment Using Lightweight Convolutional Neural Network with CBAM in Contrast Enhanced Mammography DOI
Oladosu Oyebisi Oladimeji, Hamail Ayaz, Ian McLoughlin

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 96 - 106

Published: Jan. 1, 2025

Language: Английский

Citations

0
Graph Neural Networks for Modelling Breast Biomechanical Compression DOI

Hadeel Awwad,

Eloy García, Robert Martí

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 169 - 180

Published: Jan. 1, 2025

Language: Английский

Citations

0
One for All: UNET Training on Single-Sequence Masks for Multi-sequence Breast MRI Segmentation DOI

Jarek M. van Dijk,

Luyi Han,

Luuk Balkenende

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 32 - 41

Published: Jan. 1, 2025

Language: Английский

Citations

0
Multimodal Breast MRI Language-Image Pretraining (MLIP): An Exploration of a Breast MRI Foundation Model DOI
Nika Rasoolzadeh, Tianyu Zhang, Yuan Gao

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 42 - 53

Published: Jan. 1, 2025

Language: Английский

Citations

0
MRI Breast Tissue Segmentation Using nnU-Net for Biomechanical Modeling DOI

Melika Pooyan,

Hadeel Awwad,

Eloy García

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 191 - 201

Published: Jan. 1, 2025

Language: Английский

Citations

0
Technical Review of Breast Cancer Screening and Detection using Artificial Intelligence and Radiomics DOI
Arshpreet Singh, Simranpreet Kaur,

Deepjyot Singh

et al.

2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), Journal Year: 2024, Volume and Issue: unknown, P. 1171 - 1176

Published: Feb. 28, 2024

Breast cancer is a lethal disease. Cancer occurs because of the unwanted growth cells. Globally breast (BC) increasing rapidly. BC most widespread tumor and among foremost reason cancer-related deaths in females. More alarming that it being increasingly diagnosed at younger age India compared to west. Restrictions existing imaging modalities aggravated researchers design new methods for early detection. New artificial intelligence techniques machine learning models with radiomics revolutionize medical field are becoming accepted assistive tools diagnosis prognosis BC. These have been giving admirable results superior efficiency healthcare industry applications over past decade. This paper gives detailed insight workflow radiomics, role Artificial (AI), Machine (ML), Deep (DL) treatment Various ML used detection discussed.

Language: Английский

Citations

2
Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound DOI Creative Commons

Zengan Huang,

Xin Zhang, Yan Ju

et al.

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: Sept. 19, 2024

Language: Английский

Citations

2
Contrast-enhanced mammography: better with AI? DOI
Tianyu Zhang, Ritse M. Mann

European Radiology, Journal Year: 2023, Volume and Issue: 34(2), P. 914 - 916

Published: Sept. 4, 2023

Language: Английский

Citations

4
Machine learning in healthcare strategic management: a systematic literature review DOI Creative Commons
Sand Mohammad Salhout

Arab Gulf Journal of Scientific Research, Journal Year: 2023, Volume and Issue: 42(4), P. 1530 - 1554

Published: Dec. 16, 2023

Purpose This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions enhance innovation management settings. Design/methodology/approach The papers from 2011 2021 were considered following Preferred Reporting Items for Systematic Reviews Meta-Analyses guidelines. First, relevant keywords identified, screening was performed. Bibliometric analysis One hundred twenty-three documents that passed eligibility criteria finalized. Findings Overall, annual scientific production section results reveal ML sector is growing significantly. Performing bibliometric has helped find unexplored areas; understand trend publication; categorize topics based on emerging, trending essential. paper discovers influential authors, sources, countries keywords. Research limitations/implications helps various applications institutions, such as use Internet Things healthcare, prediction disease, finding seriousness a case, natural language processing, speech language-based classification, etc. would help future researchers developers target areas are likely grow coming future. Practical implications highlights potential medical support within institutions. It suggests regression particularly promising this purpose. Hospital can leverage time series estimate number incoming patients, thus increasing hospital availability optimizing resource allocation. been instrumental development these systems. By embracing telemedicine remote monitoring, facilitate creation online patient surveillance monitoring systems, allowing early intervention ultimately improving efficiency effectiveness services. Originality/value offering comprehensive panorama ML's integration underscores pivotal role healthcare. findings contribute holistic understanding emphasize their transform optimize delivery.

Language: Английский

Citations

4
Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features DOI
Isabela Buzatto,

Sarah Abud Recife,

L. Silva Miguel

et al.

Breast Cancer Research and Treatment, Journal Year: 2024, Volume and Issue: unknown

Published: July 13, 2024

Language: Английский

Citations

0