AIMC Topic: Breast Neoplasms

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Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

Journal of the National Cancer Institute
BACKGROUND: Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior ma...

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of ove...

Automatic segmentation of breast cancer histological images based on dual-path feature extraction network.

Mathematical biosciences and engineering : MBE
The traditional manual breast cancer diagnosis method of pathological images is time-consuming and labor-intensive, and it is easy to be misdiagnosed. Computer-aided diagnosis of WSIs gradually comes into people*s sight. However, the complexity of hi...

Landscape of Artificial Intelligence in Breast Cancer (2000-2021): A Bibliometric Analysis.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Breast cancer remains one of the leading malignancies in women with distinct clinical heterogeneity and intense multidisciplinary cooperation. Remarkable progresses have been made in artificial intelligence (AI). A bibliometric analysis w...

Deep learning for survival analysis in breast cancer with whole slide image data.

Bioinformatics (Oxford, England)
MOTIVATION: Whole slide tissue images contain detailed data on the sub-cellular structure of cancer. Quantitative analyses of this data can lead to novel biomarkers for better cancer diagnosis and prognosis and can improve our understanding of cancer...

Deep learning methods for lesion detection on mammography images: a comparative analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially among women. The robust segmentation of mammography images has been considered a backbreaking task due t...

Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breast density was proven as a reliable risk factor. Deep learning models can learn subtle cues in the ma...

Deep-Learning for High Quality and High Quantitative Ultrasonic Echo Imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper performs in simulations deep learning (DL) for high quality and high quantitative ultrasonic (US) echo imaging: (i) reduction of multiple echoes (multiple reverberations) and (ii) grading lobe echoes, (iii) separation of multiply crossed w...

nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-th...

Deep Learning for Breast Cancer Classification of Deep Ultraviolet Fluorescence Images toward Intra-Operative Margin Assessment.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Breast conserving surgery aims at the complete removal of malignant lesions while minimizing healthy tissue loss. To ensure the balance between complete resection of the cancer and conservation of healthy tissue, intra-operative margin assessment is ...