[HTML][HTML] Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

BHM Van der Velden, HJ Kuijf, KGA Gilhuijs… - Medical Image …, 2022 - Elsevier
With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

A Shmatko, N Ghaffari Laleh, M Gerstung, JN Kather - Nature cancer, 2022 - nature.com
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Deep neural network models for computational histopathology: A survey

CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

EH Houssein, MM Emam, AA Ali… - Expert Systems with …, 2021 - Elsevier
Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …

[HTML][HTML] Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

S Nazir, DM Dickson, MU Akram - Computers in Biology and Medicine, 2023 - Elsevier
Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …

Bach: Grand challenge on breast cancer histology images

G Aresta, T Araújo, S Kwok, SS Chennamsetty… - Medical image …, 2019 - Elsevier
Breast cancer is the most common invasive cancer in women, affecting more than 10% of
women worldwide. Microscopic analysis of a biopsy remains one of the most important …

[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

M Salvi, UR Acharya, F Molinari… - Computers in Biology and …, 2021 - Elsevier
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients

M Shorfuzzaman, MS Hossain - Pattern recognition, 2021 - Elsevier
Various AI functionalities such as pattern recognition and prediction can effectively be used
to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and …

A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

X Li, C Li, MM Rahaman, H Sun, X Li, J Wu… - Artificial Intelligence …, 2022 - Springer
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …

Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

Z Wang, Y **ao, Y Li, J Zhang, F Lu, M Hou, X Liu - Pattern recognition, 2021 - Elsevier
The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is
an immediate priority to develop and test a computer-aided detection (CAD) scheme based …