[HTML][HTML] Distilling knowledge from an ensemble of vision transformers for improved classification of breast ultrasound
G Zhou, B Mosadegh - Academic Radiology, 2024 - Elsevier
Rationale and Objectives To develop a deep learning model for the automated classification
of breast ultrasound images as benign or malignant. More specifically, the application of …
of breast ultrasound images as benign or malignant. More specifically, the application of …
Artificial intelligence for breast US
JC Villa-Camacho, M Baikpour… - Journal of Breast …, 2023 - academic.oup.com
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 …
evaluation. It is often the primary imaging modality for the detection and diagnosis of breast …
Breast cancer screening and diagnosis: recent advances in imaging and current limitations
AR Gegios, MS Peterson, AM Fowler - PET clinics, 2023 - pet.theclinics.com
Breast cancer detection has improved over decades with increasingly advanced technology,
including digital breast tomosynthesis, breast MR imaging, and ultrasound. Breast imaging …
including digital breast tomosynthesis, breast MR imaging, and ultrasound. Breast imaging …
Domain constraints improve risk prediction when outcome data is missing
Machine learning models are often trained to predict the outcome resulting from a human
decision. For example, if a doctor decides to test a patient for disease, will the patient test …
decision. For example, if a doctor decides to test a patient for disease, will the patient test …
Classification performance assessment for imbalanced multiclass data
JS Aguilar-Ruiz, M Michalak - Scientific Reports, 2024 - nature.com
The evaluation of diagnostic systems is pivotal for ensuring the deployment of high-quality
solutions, especially given the pronounced context-sensitivity of certain systems, particularly …
solutions, especially given the pronounced context-sensitivity of certain systems, particularly …
Fully automated diagnosis of thyroid nodule ultrasound using brain-inspired inference
The interpretability of artificial intelligence (AI) based medical diagnostic systems is crucial to
make the diagnosis adequately convincible. Deep learning has been extensively …
make the diagnosis adequately convincible. Deep learning has been extensively …
Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma
T Xie, Y Wei, L Xu, Q Li, F Che, Q Xu, X Cheng… - Frontiers in …, 2023 - frontiersin.org
Background and purpose Programmed cell death protein-1 (PD-1) and programmed cell
death-ligand-1 (PD-L1) expression status, determined by immunohistochemistry (IHC) of …
death-ligand-1 (PD-L1) expression status, determined by immunohistochemistry (IHC) of …
Overlooked trustworthiness of saliency maps
Various saliency visualization methods have been proposed to explain artificial intelligence
(AI) models towards building the trustworthiness of AI-driven medical image computing …
(AI) models towards building the trustworthiness of AI-driven medical image computing …
Interpretable radiomic signature for breast microcalcification detection and classification
Breast microcalcifications are observed in 80% of mammograms, and a notable proportion
can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated …
can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated …
Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
Artificial intelligence (AI) algorithms have an enormous potential to impact the field of
radiology and diagnostic imaging, especially the field of cancer imaging. There have been …
radiology and diagnostic imaging, especially the field of cancer imaging. There have been …