AIMC Topic: Breast Neoplasms

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Classification of breast tumors by using a novel approach based on deep learning methods and feature selection.

Breast cancer research and treatment
PURPOSE: Cancer is one of the most insidious diseases that the most important factor in overcoming the cancer is early diagnosis and detection. The histo-pathological images are used to determine whether the tissue is cancerous and the type of cancer...

Deep learning for detection of iso-dense, obscure masses in mammographically dense breasts.

European radiology
OBJECTIVES: To analyze the performance of deep learning in isodense/obscure masses in dense breasts. To build and validate a deep learning (DL) model using core radiology principles and analyze its performance in isodense/obscure masses. To show perf...

Robot-assisted nipple-sparing mastectomy and immediate breast reconstruction with gel implant and latissimus dorsi muscle flap: Our initial experience.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This study reports the preliminary results of da Vinci robot XI robot-assisted nipple-sparing mastectomy immediate breast reconstruction (R-NSMIBR) with gel implant and latissimus dorsi muscle flap.

Effect of artificial intelligence-based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study.

European radiology
OBJECTIVE: To investigate whether artificial intelligence-based computer-aided diagnosis (AI-CAD) can improve radiologists' performance when used to support radiologists' interpretation of digital mammography (DM) in breast cancer screening.

Emerging uses of artificial intelligence in breast and axillary ultrasound.

Clinical imaging
Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especially in women with dense breasts. Ultrasound also plays an important role in staging breast cancer by assessing axillary lymph nodes. However, its utilit...

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI).

Physics in medicine and biology
Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However, CTV delineation is challenging as the exact extent of microscopic disease encompassed by CTV is no...

Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance.

Radiology
Background Automation bias (the propensity for humans to favor suggestions from automated decision-making systems) is a known source of error in human-machine interactions, but its implications regarding artificial intelligence (AI)-aided mammography...

PathologyBERT - Pre-trained Vs. A New Transformer Language Model for Pathology Domain.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pathology database can play a critical role to advance 'big data' cancer ...

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning.

Biochimica et biophysica acta. Molecular basis of disease
Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we coll...