[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review

Z Chen, F **ao, F Guo, J Yan - Advances in Applied Energy, 2023 - Elsevier
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review

AM Antoniadi, Y Du, Y Guendouz, L Wei, C Mazo… - Applied Sciences, 2021 - mdpi.com
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and
future potential for transforming almost all aspects of medicine. However, in many …

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

NA Wani, R Kumar, J Bedi, I Rida - Information Fusion, 2024 - Elsevier
Abstract Background and Objective: Artificial Intelligence (AI) has shown significant
advancements across several industries, including healthcare, using better fusion …

Lizard: a large-scale dataset for colonic nuclear instance segmentation and classification

S Graham, M Jahanifar, A Azam… - Proceedings of the …, 2021 - openaccess.thecvf.com
The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …

[HTML][HTML] Application of artificial intelligence in pathology: trends and challenges

I Kim, K Kang, Y Song, TJ Kim - Diagnostics, 2022 - mdpi.com
Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …

Explainable ai for medical image analysis in medical cyber-physical systems: Enhancing transparency and trustworthiness of iomt

W Liu, F Zhao, A Shankar, C Maple… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Medical image analysis plays a crucial role in healthcare systems of Internet of Medical
Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various …

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

H Evans, D Snead - Histopathology, 2024 - Wiley Online Library
Artificial intelligence (AI)‐based diagnostic tools can offer numerous benefits to the field of
histopathology, including improved diagnostic accuracy, efficiency and productivity. As a …

Conic: Colon nuclei identification and counting challenge 2022

S Graham, M Jahanifar, QD Vu… - arxiv preprint arxiv …, 2021 - arxiv.org
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained
histology images enables the extraction of interpretable cell-based features that can be used …

[HTML][HTML] Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology

JY Cheng, JT Abel, UGJ Balis, DS McClintock… - The American Journal of …, 2021 - Elsevier
Significant advances in artificial intelligence (AI), deep learning, and other machine-learning
approaches have been made in recent years, with applications found in almost every …

[Retracted] PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID‐19 with Multiple‐Way Data Augmentation

SH Wang, Y Zhang, X Cheng, X Zhang… - … Methods in Medicine, 2021 - Wiley Online Library
Aim. COVID‐19 has caused large death tolls all over the world. Accurate diagnosis is of
significant importance for early treatment. Methods. In this study, we proposed a novel …