AIMC Topic: Breast Neoplasms

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Role of artificial intelligence -based machine learning model in predicting HER2/neu gene status in breast cancer.

Pathology, research and practice
Our study investigated the predictive efficacy of AI-based Machine Learning (ML) model for determining HER2 status in a population of 3424 breast cancer patients. Multivariate logistic regression analysis identified several independent variables that...

Review and reflections on live AI mammographic screen reading in a large UK NHS breast screening unit.

Clinical radiology
UNLABELLED: The Radiology team from a large Breast Screening Unit in the UK with a screening population of over 135,000 took part in a service evaluation project using artificial intelligence (AI) for reading breast screening mammograms.

A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification.

Journal of biomedical informatics
OBJECTIVES: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simp...

Large language models in breast cancer reconstruction: A framework for patient-specific recovery and predictive insights.

SLAS technology
Breast cancer reconstruction, a vital part of comprehensive cancer therapy, can be performed concurrently with cancer resection, improving both physical and psychological recovery for patients. However, the intricacy and variety of recovery demand a ...

Mitosis detection and classification for breast cancer diagnosis: What we know and what is next.

Computers in biology and medicine
Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine...

Computational pathology for breast cancer: Where do we stand for prognostic applications?

Breast (Edinburgh, Scotland)
The very early days of artificial intelligence (AI) in healthcare are behind us. AI is now spreading in the healthcare sector and is gradually being implemented in routine clinical practice. Driven by the increasing digitization of microscope slides,...

FedBCD: Federated Ultrasound Video and Image Joint Learning for Breast Cancer Diagnosis.

IEEE transactions on medical imaging
Ultrasonography plays an essential role in breast cancer diagnosis. Current deep learning based studies train the models on either images or videos in a centralized learning manner, lacking consideration of joint benefits between two different modali...

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Breast cancer research and treatment
PURPOSE: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.

Scientific reports
Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet...

Mammogram mastery: Breast cancer image classification using an ensemble of deep learning with explainable artificial intelligence.

Medicine
Breast cancer is a serious public health problem and is one of the leading causes of cancer-related deaths in women worldwide. Early detection of the disease can significantly increase the chances of survival. However, manual analysis of mammogram ma...