AIMC Topic: Breast Neoplasms

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HARVESTMAN: a framework for hierarchical feature learning and selection from whole genome sequencing data.

BMC bioinformatics
BACKGROUND: Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that re...

A deep learning classifier for digital breast tomosynthesis.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To develop a computerized detection system for the automatic classification of the presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, based on a deep convolutional neural network (DCNN).

Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival.

Scientific reports
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical met...

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.

BMC genomics
BACKGROUND: Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival an...

An Intuitionistic Fuzzy Clustering Approach for Detection of Abnormal Regions in Mammogram Images.

Journal of digital imaging
Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very cr...

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer.

Scientific reports
This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy...

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System.

Journal of healthcare engineering
Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic ...

Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep learning models.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To develop a deep learning model capable of producing clinically acceptable dose distributions for left-sided breast cancers for 3D-CRT while exploring the use of two-dimensional versus three-dimensional anatomical data.

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.

Scientific reports
The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework "M...

Artificial intelligence for the real world of breast screening.

European journal of radiology
Breast cancer screening with mammography reduces mortality in the women who attend by detecting high risk cancer early. It is far from perfect with variations in both sensitivity for the detection of cancer and very wide variations in specificity, le...