AIMC Topic: Breast

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Robust 3D breast reconstruction based on monocular images and artificial intelligence for robotic guided oncological interventions.

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 a global public health concern. For women with suspicious breast lesions, the current diagnosis requires a biopsy, which is usually guided by ultrasound (US). However, this process is challenging due to the low quality of the US imag...

Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accurate lesion classification as benign or malignant in breast ultrasound (BUS) images is a critical task that requires experienced radiologists and has many challenges, such as poor image quality, artifacts, and high lesion variability. Thus, autom...

Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced Mammograms.

Radiology
Background Handcrafted radiomics and deep learning (DL) models individually achieve good performance in lesion classification (benign vs malignant) on contrast-enhanced mammography (CEM) images. Purpose To develop a comprehensive machine learning too...

Artificial Intelligence for Breast US.

Journal of breast imaging
US is a widely available, commonly used, and indispensable imaging modality for breast evaluation. It is often the primary imaging modality for the detection and diagnosis of breast cancer in low-resource settings. In addition, it is frequently emplo...

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis.

Saudi medical journal
Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial i...

IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation.

Mathematical biosciences and engineering : MBE
Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician's diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the firs...

Deep learning for differentiating benign from malignant tumors on breast-specific gamma image.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Breast diseases are a significant health threat for women. With the fast-growing BSGI data, it is becoming increasingly critical for physicians to accurately diagnose benign as well as malignant breast tumors.

Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer.

Clinical hemorheology and microcirculation
OBJECTIVES: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC).

New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis.

Radiographics : a review publication of the Radiological Society of North America, Inc
The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower recall rates compared with those achieved by using full-field digital mammography (DM). However, th...

Evaluation of voice function after BABA robotic thyroid lobectomy: A comparative analysis with endoscopic thyroid lobectomy.

Medicine
The purpose of this study was to compare the effect of robotic thyroid lobectomy via Bilateral Axlio-Breast Approach (BABA) and endoscopic thyroid lobectomy on the voice function. A total of 125 patients with thyroid cancer from March 2021 to July 20...