AI Medical Compendium Topic

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Breast

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Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis.

Radiological physics and technology
To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for inf...

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

Medical physics
BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Ea...

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

Medical physics
BACKGROUND: Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative...

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.

Scientific reports
The objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) pr...

Fourier ptychographic and deep learning using breast cancer histopathological image classification.

Journal of biophotonics
Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep l...

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Tomography (Ann Arbor, Mich.)
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess ris...

A divide and conquer approach to maximise deep learning mammography classification accuracies.

PloS one
Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However,...

Robot-Assisted Capsulectomy with Immediate Reimplantation in Breast Reconstruction.

Plastic and reconstructive surgery
Recent reports have introduced robotic breast surgery for immediate breast reconstruction with an implant. However, relevant reports of robot-assisted breast reconstruction including capsulectomy are limited. Although capsulectomy lowers risk of caps...

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning.

Journal of digital imaging
IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification method called ...