Algorithm fairness in ai for medicine and healthcare
In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
AI in computational pathology of cancer: improving diagnostic workflows and clinical outcomes?
D Cifci, GP Veldhuizen, S Foersch… - Annual Review of …, 2023 - annualreviews.org
Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and
has become a cornerstone of modern precision oncology. Histopathological evaluation is …
has become a cornerstone of modern precision oncology. Histopathological evaluation is …
Deep learning generates synthetic cancer histology for explainability and education
JM Dolezal, R Wolk, HM Hieromnimon… - NPJ precision …, 2023 - nature.com
Artificial intelligence methods including deep neural networks (DNN) can provide rapid
molecular classification of tumors from routine histology with accuracy that matches or …
molecular classification of tumors from routine histology with accuracy that matches or …
Gender artifacts in visual datasets
Gender biases are known to exist within large-scale visual datasets and can be reflected or
even amplified in downstream models. Many prior works have proposed methods for …
even amplified in downstream models. Many prior works have proposed methods for …
The utility of color normalization for ai‐based diagnosis of hematoxylin and eosin‐stained pathology images
J Boschman, H Farahani, A Darbandsari… - The Journal of …, 2022 - Wiley Online Library
The color variation of hematoxylin and eosin (H&E)‐stained tissues has presented a
challenge for applications of artificial intelligence (AI) in digital pathology. Many color …
challenge for applications of artificial intelligence (AI) in digital pathology. Many color …
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides
Cancer diagnosis and management depend upon the extraction of complex information from
microscopy images by pathologists, which requires time-consuming expert interpretation …
microscopy images by pathologists, which requires time-consuming expert interpretation …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer
Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor
microenvironment of breast cancer, but substantial interobserver variability among …
microenvironment of breast cancer, but substantial interobserver variability among …
Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence
Gene expression-based recurrence assays are strongly recommended to guide the use of
chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing …
chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing …
Proportionally fair hospital collaborations in federated learning of histopathology images
Medical centers and healthcare providers have concerns and hence restrictions around
sharing data with external collaborators. Federated learning, as a privacy-preserving …
sharing data with external collaborators. Federated learning, as a privacy-preserving …