Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
[HTML][HTML] Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models
Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …
under a microscope into a computer vision workflow. Whole slide imaging allows …
Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …
pathology slides in colorectal cancer (CRC). However, current approaches rely on …
Multimodal deep learning for integrating chest radiographs and clinical parameters: a case for transformers
Background Clinicians consider both imaging and nonimaging data when diagnosing
diseases; however, current machine learning approaches primarily consider data from a …
diseases; however, current machine learning approaches primarily consider data from a …
Nbias: A natural language processing framework for BIAS identification in text
Bias in textual data can lead to skewed interpretations and outcomes when the data is used.
These biases could perpetuate stereotypes, discrimination, or other forms of unfair …
These biases could perpetuate stereotypes, discrimination, or other forms of unfair …
An MRI deep learning model predicts outcome in rectal cancer
Background Deep learning (DL) models can potentially improve prognostication of rectal
cancer but have not been systematically assessed. Purpose To develop and validate an MRI …
cancer but have not been systematically assessed. Purpose To develop and validate an MRI …
[HTML][HTML] Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study
Background Deep learning is a promising way to improve health care. Image-processing
medical disciplines, such as pathology, are expected to be transformed by deep learning …
medical disciplines, such as pathology, are expected to be transformed by deep learning …
AI in computational pathology of cancer: improving diagnostic workflows and clinical outcomes?
Histopathology plays a fundamental role in the diagnosis and subty** 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 …
Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications
The current study investigates the robustness of deep learning models for accurate medical
diagnosis systems with a specific focus on their ability to maintain performance in the …
diagnosis systems with a specific focus on their ability to maintain performance in the …
Advmil: Adversarial multiple instance learning for the survival analysis on whole-slide images
The survival analysis on histological whole-slide images (WSIs) is one of the most important
means to estimate patient prognosis. Although many weakly-supervised deep learning …
means to estimate patient prognosis. Although many weakly-supervised deep learning …