Semi-supervised learning in cancer diagnostics
In cancer diagnostics, a considerable amount of data is acquired during routine work-up.
Recently, machine learning has been used to build classifiers that are tasked with cancer …
Recently, machine learning has been used to build classifiers that are tasked with cancer …
Rise of deep learning clinical applications and challenges in omics data: a systematic review
This research aims to review and evaluate the most relevant scientific studies about deep
learning (DL) models in the omics field. It also aims to realize the potential of DL techniques …
learning (DL) models in the omics field. It also aims to realize the potential of DL techniques …
Patient graph deep learning to predict breast cancer molecular subtype
I Furtney, R Bradley, MR Kabuka - IEEE/ACM transactions on …, 2023 - ieeexplore.ieee.org
Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations
and clinical characteristics. The molecular subtypes of breast cancer are closely tied to …
and clinical characteristics. The molecular subtypes of breast cancer are closely tied to …
XAI-MethylMarker: Explainable AI approach for biomarker discovery for breast cancer subtype classification using methylation data
Breast cancer—a heterogeneous disease marked with a high mortality rate, necessitates
early diagnosis and treatment. The availability of multi-omic data has revolutionized our …
early diagnosis and treatment. The availability of multi-omic data has revolutionized our …
XAI-CNVMarker: Explainable AI-based copy number variant biomarker discovery for breast cancer subtypes
Breast cancer is a leading cause of cancer-related deaths among women. The multi-omic
data has revolutionized the methodology to unravel molecular heterogeneity in breast …
data has revolutionized the methodology to unravel molecular heterogeneity in breast …
Deep Learning‐Based Multiomics Data Integration Methods for Biomedical Application
Y Wen, L Zheng, D Leng, C Dai, J Lu… - Advanced Intelligent …, 2023 - Wiley Online Library
The innovation of high‐throughput technologies and medical radiomics allows biomedical
data to accumulate at an astonishing rate. Several promising deep learning (DL) methods …
data to accumulate at an astonishing rate. Several promising deep learning (DL) methods …
Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward
We present a survey of the current state-of-the-art in breast cancer detection and prognosis.
We analyze the evolution of Artificial Intelligence-based approaches from using just uni …
We analyze the evolution of Artificial Intelligence-based approaches from using just uni …
View-aware collaborative learning for survival prediction and subgroup identification
Advances of high throughput experimental methods have led to the availability of more
diverse omic datasets in clinical analysis applications. Different types of omic data reveal …
diverse omic datasets in clinical analysis applications. Different types of omic data reveal …
PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
Due to the high heterogeneity and clinical characteristics of cancer, there are significant
differences in multi-omic data and clinical characteristics among different cancer subtypes …
differences in multi-omic data and clinical characteristics among different cancer subtypes …
Automated Defect Detection in Mass-Produced Electronic Components via YOLO Object Detection Models
Since the defect detection of conventional industry components is time-consuming and labor-
intensive, it leads to a significant burden on quality inspection personnel and difficult to …
intensive, it leads to a significant burden on quality inspection personnel and difficult to …