AIMC Topic: Mammography

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Computer-aided diagnosis of mammographic masses using scalable image retrieval.

IEEE transactions on bio-medical engineering
Computer-aided diagnosis of masses in mammograms is important to the prevention of breast cancer. Many approaches tackle this problem through content-based image retrieval techniques. However, most of them fall short of scalability in the retrieval s...

Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection.

Computers in biology and medicine
In this paper, a new method is developed for extracting so-called region-based stellate features to correctly differentiate spiculated malignant masses from normal tissues on mammograms. In the proposed method, a given region of interest (ROI) for fe...

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

Journal of digital imaging
This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate t...

Do We Still Need Randomized Controlled Trials to Support Use of New Methods of Breast Cancer Screening?

Journal of breast imaging
Randomized controlled trials (RCTs) have confirmed the mortality benefits of screening mammography and are the gold standard for evaluating new diagnostic tests and medical interventions. Reliable and rigorous execution of RCTs can be complex and req...

Improved Breast Cancer Detection with Artificial Intelligence in a Real-World Digital Breast Tomosynthesis Screening Program.

Clinical breast cancer
OBJECTIVE: The purpose of this study is to compare radiologists' breast cancer screening performance before and after the implementation of an artificial intelligence (AI) detection system for digital breast tomosynthesis (DBT).

Artificial Intelligence for Assessment of Digital Mammography Positioning Reveals Persistent Challenges.

Journal of breast imaging
OBJECTIVE: Mammographic breast cancer detection depends on high-quality positioning, which is traditionally assessed and monitored subjectively. This study used artificial intelligence (AI) to evaluate mammography positioning on digital screening mam...

Bias in Artificial Intelligence: Impact on Breast Imaging.

Journal of breast imaging
Artificial intelligence (AI) in breast imaging has garnered significant attention given the numerous reports of improved efficiency, accuracy, and the potential to bridge the gap of expanded volume in the face of limited physician resources. While AI...

Advancements in Detection and Management of Ductal Carcinoma in Situ.

Radiographics : a review publication of the Radiological Society of North America, Inc
Ductal carcinoma in situ (DCIS) is a noninvasive breast cancer characterized by neoplastic epithelial cells confined to the ductal system by the basement membrane without invasion of adjacent tissue. Its progression to invasive carcinoma is not under...

Development of a deep learning-based automated diagnostic system (DLADS) for classifying mammographic lesions - a first large-scale multi-institutional clinical trial in Japan.

Breast cancer (Tokyo, Japan)
BACKGROUND: Recently, western countries have built evidence on mammographic artificial Intelligence-computer-aided diagnosis (AI-CADx) systems; however, their effectiveness has not yet been sufficiently validated in Japanese women. In this study, we ...

Artificial Intelligence Language Models to Translate Professional Radiology Mammography Reports Into Plain Language - Impact on Interpretability and Perception by Patients.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to evaluate the interpretability and patient perception of AI-translated mammography and sonography reports, focusing on comprehensibility, follow-up recommendations, and conveyed empathy using a survey.