The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues. This study introduces a novel data augmentation technique termed Attention-Guided Erasing (AGE), devised to enhance the downstream classification of four distinct breast density categories in mammography following the BI-RADS recommendation in the Vietnamese cohort. The proposed method integrates supplementary information during transfer learning, utilizing visual attention maps derived from a vision transformer backbone trained using the self-supervised DINO method. These maps are utilized to erase background regions in the mammogram images, unveiling only the potential areas of dense breast tissues to the network. Through the incorporation of AGE during transfer learning with varying random probabilities, we consistently surpass classification performance compared to scenarios without AGE and the traditional random erasing transformation. We validate our methodology using the publicly available VinDr-Mammo dataset. Specifically, we attain a mean F1-score of 0.5910, outperforming values of 0.5594 and 0.5691 corresponding to scenarios without AGE and with random erasing (RE), respectively. This superiority is further substantiated by t-tests, revealing a p-value of p<0.0001, underscoring the statistical significance of our approach.

Breast cancer is the second most common cancer globally, and is the most commonly diagnosed cancer in Indian women. Of the 685,000 women who die around the world every year because of breast cancer, over 90,000 are in India, where cancer of the breast is the most common cause of cancer-related deaths in India. One of the major reasons for the high mortality rate in India is that most Indian patients present in the later stages of the disease. Population-scale screening with early detection methods, and efforts to increase awareness of breast cancer, could help tackle the disease, improve survival rates and reduce treatment costs. Screening mammography is a widely used method, but its usage in low- and middle-income countries (LMICs) is limited due to equipment cost and the expert skill required for interpretation of mammograms.

October 07, 2021 – With artificial intelligence technology, medical professionals can quickly and accurately sort through breast MRIs in patients with dense breast tissue to eliminate those without cancer. Mammography has assisted in reducing breast cancer-related deaths by providing early detection when cancer is still treatable. However, it is less sensitive in women with extremely dense breast tissue than fatty breast tissue. Additionally, women with extremely dense breasts are three to six times more likely to develop breast cancer than women with almost entirely fatty breasts and two times more likely than the average woman.

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online Sept. 24 in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, Ph.D. Ultrasound exams use high-frequency sound waves passing through tissue to construct real-time images of breast or other tissues.

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online Sept. 24 in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, PhD.

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online today (September 24, 2021) in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, PhD.

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online Sept. 24 in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, PhD.

Breast cancer affects one in eight women in the US. There are multiple factors involved in developing the disease, but one issue is dense breast tissue. Some 40 percent of US women have dense breast tissue, which alone increases the risk of breast cancer, and can make mammogram screening more difficult. Now, researchers from MIT and Massachusetts General Hospital (MGH) have developed an automated model that assesses dense breast tissue in mammograms as reliably as expert radiologists. Breast density assessments have traditionally relied on subjective human exams and calculations, but the deep-learning model -- trained on tens of thousands of digital mammograms -- is able to distinguish different types of breast tissue, from fatty to extremely dense, with 90 percent correlation to radiologists' diagnosis.