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 …
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …
[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 …
[HTML][HTML] Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI
AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
[HTML][HTML] Fine-tuning and training of densenet for histopathology image representation using tcga diagnostic slides
Feature vectors provided by pre-trained deep artificial neural networks have become a
dominant source for image representation in recent literature. Their contribution to the …
dominant source for image representation in recent literature. Their contribution to the …
Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer
SM Thomas, JG Lefevre, G Baxter, NA Hamilton - Medical Image Analysis, 2021 - Elsevier
We apply for the first-time interpretable deep learning methods simultaneously to the most
common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal …
common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal …
Toward human–AI interfaces to support explainability and causability in medical AI
A Holzinger, H Müller - Computer, 2021 - ieeexplore.ieee.org
Our concept of causability is a measure of whether and to what extent humans can
understand a given machine explanation. We motivate causability with a clinical case from …
understand a given machine explanation. We motivate causability with a clinical case from …
[HTML][HTML] Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors
Z Li, Y Cong, X Chen, J Qi, J Sun, T Yan, H Yang, J Liu… - IScience, 2023 - cell.com
Diagnosis of primary brain tumors relies heavily on histopathology. Although various
computational pathology methods have been developed for automated diagnosis of primary …
computational pathology methods have been developed for automated diagnosis of primary …
Artificial intelligence and cellular segmentation in tissue microscopy images
With applications in object detection, image feature extraction, image classification, and
image segmentation, artificial intelligence is facilitating high-throughput analysis of image …
image segmentation, artificial intelligence is facilitating high-throughput analysis of image …
Explainable AI and multi-modal causability in medicine
A Holzinger - i-com, 2021 - degruyter.com
Progress in statistical machine learning made AI in medicine successful, in certain
classification tasks even beyond human level performance. Nevertheless, correlation is not …
classification tasks even beyond human level performance. Nevertheless, correlation is not …
Explainable AI for chiller fault-detection systems: gaining human trust
Explainable AI for Chiller Fault-Detection Systems: Gaining Human Trust Page 1 60
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COMPUTER PUBLISHED BY THE IEEE COMPUTER SOCIETY 0018-9162/21©20211EEE …