AIMC Topic: Breast

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Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study.

British journal of cancer
BACKGROUND: This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM.

Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer.

Human pathology
Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports o...

Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views.

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.

European radiology
OBJECTIVES: To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously.

A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images.

Medical image analysis
Mitosis counting of biopsies is an important biomarker for breast cancer patients, which supports disease prognostication and treatment planning. Developing a robust mitotic cell detection model is highly challenging due to its complex growth pattern...

Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.

Tomography (Ann Arbor, Mich.)
Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is importa...

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer.

Radiology
Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted...

Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the perform...