| 1. |
- Asa, Sylvia, et al.
(författare)
-
2020 vision of digital pathology in action
- 2019
-
Ingår i: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 10:27
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
|
|
| 2. |
- Capitanio, Arrigo, et al.
(författare)
-
Digital cytology: A short review of technical and methodological approaches and applications
- 2018
-
Ingår i: Cytopathology. - : WILEY. - 0956-5507 .- 1365-2303. ; 29:4, s. 317-325
-
Forskningsöversikt (refereegranskat)abstract
- The recent years have been characterised by a rapid development of whole slide imaging (WSI) especially in its applications to histology. The application of WSI technology to cytology is less common because of technological problems related to the three-dimensional nature of cytology preparations (which requires capturing of z-stack information, with an increase in file size and usability issues in viewing cytological preparations). The aim of this study is to provide a review of the literature on the use of digital cytology and provide an overview of cytological applications of WSI in current practice as well as identifying areas for future development.
|
|
| 3. |
- Falk, Martin, Dr.rer.nat. 1981-, et al.
(författare)
-
Interactive Visualization of 3D Histopathology in Native Resolution
- 2019
-
Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1077-2626 .- 1941-0506 .- 2160-9306. ; 25:1, s. 1008-1017
-
Tidskriftsartikel (refereegranskat)abstract
- We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 x 100,000 x 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- King, Henry, et al.
(författare)
-
How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study
- 2023
-
Ingår i: JAMIA Journal of the American Medical Informatics Association. - : OXFORD UNIV PRESS. - 1067-5027 .- 1527-974X. ; 30:3, s. 529-538
-
Tidskriftsartikel (refereegranskat)abstract
- Objective There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders theories concerning how and in what contexts AI is likely to become integrated into pathology. Materials and Methods A literature review provided tentative theories that were revised through a realist interview study with 20 pathologists and 5 pathology trainees. Questions sought to elicit whether, and in what ways, the tentative theories fitted with interviewees perceptions and experiences. Analysis focused on identifying the contextual factors that may support or constrain uptake of AI in pathology. Results Interviews highlighted the importance of trust in AI, with interviewees emphasizing evaluation and the opportunity for pathologists to become familiar with AI as means for establishing trust. Interviewees expressed a desire to be involved in design and implementation of AI tools, to ensure such tools address pressing needs, but needs vary by subspecialty. Workflow integration is desired but whether AI tools should work automatically will vary according to the task and the context. Conclusions It must not be assumed that AI tools that provide benefit in one subspecialty will provide benefit in others. Pathologists should be involved in the decision to introduce AI, with opportunity to assess strengths and weaknesses. Further research is needed concerning the evidence required to satisfy pathologists regarding the benefits of AI.
|
|
| 7. |
- Lindvall, Martin
(författare)
-
Designing with Machine Learning in Digital Pathology : Augmenting Medical Specialists through Interaction Design
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recent advancements in machine learning (ML) have led to a dramatic increase in AI capabilities for medical diagnostic tasks. Despite technical advances, developers of predictive AI models struggle to integrate their work into routine clinical workflows. Inefficient human-AI interactions, poor sociotechnical fit and a lack of interactive strategies for dealing with the imperfect nature of predictions are known factors contributing to this lack of adoption.User-centred design methods are typically aimed at discovering and realising desirable qualities in use, pragmatically oriented around finding solutions despite the limitations of material- and human resources. However, existing methods often rely on designers possessing knowledge of suitable interactive metaphors and idioms, as well as skills in evaluating ideas through low-fidelity prototyping and rapid iteration methods—all of which are challenged by the data-driven nature of machine learning and the unpredictable outputs from AI models.Using a constructive design research approach, my work explores how we might design systems with AI components that aid clinical decision-making in a human-centred and iterative fashion. Findings are derived from experiments and experiences from four exploratory projects conducted in collaboration with professional physicians, all aiming to probe this design space by producing novel interactive systems for or with ML components.Contributions include identifying practical and theoretical design challenges, suggesting novel interaction strategies for human-AI collaboration, framing ML competence for designers and presenting empirical descriptions of conducted design processes. Specifically, this compilation thesis contains three works that address effective human-machine teaching and two works that address the challenge of designing interactions that afford successful decision-making despite the uncertainty and imperfections inherent in machine predictions.Finally, two works directly address design-researchers working with ML, arguing for a systematic approach to increase the repertoire available for theoretical annotation and understanding of the properties of ML as a designerly material.
|
|
| 8. |
- Lindvall, Martin, et al.
(författare)
-
TissueWand, a rapid histopathology annotation tool
- 2020
-
Ingår i: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 11:27
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Recent advancements in machine learning (ML) bring great possibilities for the development of tools to assist with diagnostic tasks within histopathology. However, these approaches typically require a large amount of ground truth training data in the form of image annotations made by human experts. As such annotation work is a very time-consuming task, there is a great need for tools that can assist in this process, saving time while not sacrificing annotation quality. Methods: In an iterative design process, we developed TissueWand – an interactive tool designed for efficient annotation of gigapixel-sized histopathological images, not being constrained to a predefined annotation task. Results: Several findings regarding appropriate interaction concepts were made, where a key design component was semi-automation based on rapid interaction feedback in a local region. In a user study, the resulting tool was shown to cause substantial speed-up compared to manual work while maintaining quality. Conclusions: The TissueWand tool shows promise to replace manual methods for early stages of dataset curation where no task-specific ML model yet exists to aid the effort.
|
|
| 9. |
- Molin, Jesper, 1987, et al.
(författare)
-
Scale Stain: Multi-Resolution Feature Enhancement in Pathology Visualization
- 2016
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Digital whole-slide images of pathological tissue samples have recently become feasible for use within routine diagnostic practice. These gigapixel sized images enable pathologists to perform reviews using computer workstations instead of microscopes. Existing workstations visualize scanned images by providing a zoomable image space that reproduces the capabilities of the microscope. This paper presents a novel visualization approach that enables filtering of the scale-space according to color preference. The visualization method reveals diagnostically important patterns that are otherwise not visible. The paper demonstrates how thisapproach has been implemented into a fully functional prototype that lets the user navigate the visualization parameter space in real time. The prototype was evaluated for two common clinical tasks with eight pathologists in a within-subjects study. The data reveal thattask efficiency increased by 15% using the prototype, with maintained accuracy. By analyzing behavioral strategies, it was possible to conclude that efficiency gain was caused by a reduction of the panning needed to perform systematic search of the images. The prototype system was well received by the pathologists who did not detect any risks that would hinder use in clinical routine.
|
|
| 10. |
- Skoglund, Karin, 1980-, et al.
(författare)
-
Annotations, ontologies, and whole slide images : Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue
- 2019
-
Ingår i: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 10:22
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information. Materials and Methods: Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts. Results: Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm2, equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h. Conclusion: This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.
|
|
