LIVIVO - Search results -

Search results

Result 1 - 10 of total 80

Search options

Article: On image search in histopathology.

Tizhoosh, H R / Pantanowitz, Liron

2024 Volume 15, Page(s) 100375

Abstract: Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent ... ...

Abstract	Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.
Language	English
Publishing date	2024-04-04
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2579241-6
ISSN	2153-3539 ; 2229-5089
ISSN (online)	2153-3539
ISSN	2229-5089
DOI	10.1016/j.jpi.2024.100375
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Article ; Online: A self-supervised framework for cross-modal search in histopathology archives using scale harmonization.

Maleki, Danial / Rahnamayan, Shahryar / Tizhoosh, H R

Scientific reports

2024 Volume 14, Issue 1, Page(s) 9724

Abstract: The exponential growth of data across various medical domains has generated a substantial demand for techniques to analyze multimodal big data. This demand is particularly pronounced in fields such as computational pathology due to the diverse nature of ... ...

Abstract	The exponential growth of data across various medical domains has generated a substantial demand for techniques to analyze multimodal big data. This demand is particularly pronounced in fields such as computational pathology due to the diverse nature of the tissue. Cross-modal retrieval aims to identify a common latent space where different modalities, such as image-text pairs, exhibit close alignment. The primary challenge, however, often lies in the representation of tissue features. While language models can be trained relatively easily, visual models frequently struggle due to the scarcity of labeled data. To address this issue, the innovative concept of harmonization has been introduced, extending the learning scheme distillation without supervision, known as DINO. The harmonization of scale refines the DINO paradigm through a novel patching approach, overcoming the complexities posed by gigapixel whole slide images in digital pathology. Experiments conducted on diverse datasets have demonstrated that the proposed approach significantly enhances cross-modal retrieval in tissue imaging. Moreover, it exhibits vast potential for other fields that rely on gigapixel imaging.
MeSH term(s)	Humans ; Image Processing, Computer-Assisted/methods ; Algorithms ; Archives
Language	English
Publishing date	2024-04-27
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-60256-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Book ; Online: On Image Search in Histopathology

Tizhoosh, H. R. / Pantanowitz, Liron

2024

Abstract: Pathology images of histopathology can be acquired from camera-mounted microscopes or whole slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent ... ...

Abstract	Pathology images of histopathology can be acquired from camera-mounted microscopes or whole slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for nuanced quantification of cellular structures across diverse tissue types, facilitating comparisons and enabling inferences about diagnosis, prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this paper, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast and efficient image search methods in their work.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Information Retrieval ; Quantitative Biology - Quantitative Methods
Subject code	006
Publishing date	2024-01-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: A Similarity Measure of Histopathology Images by Deep Embeddings.

Afshari, Mehdi / Tizhoosh, H R

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2021 Volume 2021, Page(s) 3447–3450

Abstract: Histopathology digital scans are large-size images that contain valuable information at the pixel level. Contentbased comparison of these images is a challenging task. This study proposes a content-based similarity measure for highresolution gigapixel ... ...

Abstract	Histopathology digital scans are large-size images that contain valuable information at the pixel level. Contentbased comparison of these images is a challenging task. This study proposes a content-based similarity measure for highresolution gigapixel histopathology images. The proposed similarity measure is an expansion of cosine vector similarity to a matrix. Each image is divided into same-size patches with a meaningful amount of information (i.e., contained enough tissue). The similarity is measured by the extraction of patchlevel deep embeddings of the last pooling layer of a pre-trained deep model at four different magnification levels, namely, 1x, 2.5x, 5x, and 10x magnifications. In addition, for faster measurement, embedding reduction is investigated. Finally, to assess the proposed method, an image search method is implemented. Results show that the similarity measure represents the slide labels with a maximum accuracy of 93.18% for top-5 search at 5x magnification.
Language	English
Publishing date	2021-12-07
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC46164.2021.9630818
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Meroueh, Chady / Warasnhe, Khaled / Tizhoosh, Hamid R / Shah, Vijay H / Ibrahim, Samar H

Hepatology (Baltimore, Md.)

2024

Abstract: Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver ... ...

Abstract	Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.
Language	English
Publishing date	2024-03-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	604603-4
ISSN	1527-3350 ; 0270-9139
ISSN (online)	1527-3350
ISSN	0270-9139
DOI	10.1097/HEP.0000000000000866
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1660: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Immunohistochemistry Biomarkers-Guided Image Search for Histopathology.

Shafique, Abubakr / Babaie, Morteza / Gonzalez, Ricardo / Tizhoosh, H R

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2023 Volume 2023, Page(s) 1–5

Abstract: ... Haematoxylin and Eosin (H&E) stained glass slides have been used for cancer diagnosis and tumor detection ... and confirm diagnoses for determining the subtype of a tumor when this is difficult using H&E slides ... than a conventional (i.e., H&E-only) search engine without IHC guidance. Moreover, such engines are also able ...

Abstract	Medical practitioners use a number of diagnostic tests to make a reliable diagnosis. Traditionally, Haematoxylin and Eosin (H&E) stained glass slides have been used for cancer diagnosis and tumor detection. However, recently a variety of immunohistochemistry (IHC) stained slides can be requested by pathologists to examine and confirm diagnoses for determining the subtype of a tumor when this is difficult using H&E slides only. Deep learning (DL) has received a lot of interest recently for image search engines to extract features from tissue regions, which may or may not be the target region for diagnosis. This approach generally fails to capture high-level patterns corresponding to the malignant or abnormal content of histopathology images. In this work, we are proposing a targeted image search approach, inspired by the pathologists' workflow, which may use information from multiple IHC biomarker images when available. These IHC images could be aligned, filtered, and merged together to generate a composite biomarker image (CBI) that could eventually be used to generate an attention map to guide the search engine for localized search. In our experiments, we observed that an IHC-guided image search engine can retrieve relevant data more accurately than a conventional (i.e., H&E-only) search engine without IHC guidance. Moreover, such engines are also able to accurately conclude the subtypes through majority votes.
MeSH term(s)	Humans ; Immunohistochemistry ; Neoplasms ; Biomarkers, Tumor
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340099
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Learning binary and sparse permutation-invariant representations for fast and memory efficient whole slide image search.

Hemati, Sobhan / Kalra, Shivam / Babaie, Morteza / Tizhoosh, H R

Computers in biology and medicine

2023 Volume 162, Page(s) 107026

Abstract: Considering their gigapixel sizes, the representation of whole slide images (WSIs) for classification and retrieval systems is a non-trivial task. Patch processing and multi-Instance Learning (MIL) are common approaches to analyze WSIs. However, in end- ... ...

Abstract	Considering their gigapixel sizes, the representation of whole slide images (WSIs) for classification and retrieval systems is a non-trivial task. Patch processing and multi-Instance Learning (MIL) are common approaches to analyze WSIs. However, in end-to-end training, these methods require high GPU memory consumption due to the simultaneous processing of multiple sets of patches. Furthermore, compact WSI representations through binary and/or sparse representations are urgently needed for real-time image retrieval within large medical archives. To address these challenges, we propose a novel framework for learning compact WSI representations utilizing deep conditional generative modeling and the Fisher Vector Theory. The training of our method is instance-based, achieving better memory and computational efficiency during the training. To achieve efficient large-scale WSI search, we introduce new loss functions, namely gradient sparsity and gradient quantization losses, for learning sparse and binary permutation-invariant WSI representations called Conditioned Sparse Fisher Vector (C-Deep-SFV), and Conditioned Binary Fisher Vector (C-Deep-BFV). The learned WSI representations are validated on the largest public WSI archive, The Cancer Genomic Atlas (TCGA) and also Liver-Kidney-Stomach (LKS) dataset. For WSI search, the proposed method outperforms Yottixel and Gaussian Mixture Model (GMM)-based Fisher Vector both in terms of retrieval accuracy and speed. For WSI classification, we achieve competitive performance against state-of-art on lung cancer data from TCGA and the public benchmark LKS dataset.
MeSH term(s)	Learning ; Benchmarking ; Genomics ; Kidney ; Liver
Language	English
Publishing date	2023-05-22
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107026
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 653: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Class-imbalanced Unsupervised and Semi-Supervised Domain Adaptation for Histopathology Images.

Hosseini, S Maryam / Shafique, Abubakr / Babaie, Morteza / Tizhoosh, H R

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2023 Volume 2023, Page(s) 1–7

Abstract: In dealing with the lack of sufficient annotated data and in contrast to supervised learning, unsupervised, self-supervised, and semi-supervised domain adaptation methods are promising approaches, enabling us to transfer knowledge from rich labeled ... ...

Abstract	In dealing with the lack of sufficient annotated data and in contrast to supervised learning, unsupervised, self-supervised, and semi-supervised domain adaptation methods are promising approaches, enabling us to transfer knowledge from rich labeled source domains to different (but related) unlabeled target domains, reducing distribution discrepancy between the source and target domains. However, most existing domain adaptation methods do not consider the imbalanced nature of the real-world data, affecting their performance in practice. We propose to overcome this limitation by proposing a novel domain adaptation approach that includes two modifications to the existing models. Firstly, we leverage the focal loss function in response to class-imbalanced labeled data in the source domain. Secondly, we introduce a novel co-training approach to involve pseudo-labeled target data points in the training process. Experiments show that the proposed model can be effective in transferring knowledge from source to target domain. As an example, we use the classification of prostate cancer images into low-cancerous and high-cancerous regions.
MeSH term(s)	Humans ; Male ; Prostatic Neoplasms ; Pathology ; Image Interpretation, Computer-Assisted
Language	English
Publishing date	2023-12-27
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340049
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Whole slide image representation in bone marrow cytology.

Mu, Youqing / Tizhoosh, H R / Dehkharghanian, Taher / Campbell, Clinton J V

Computers in biology and medicine

2023 Volume 166, Page(s) 107530

Abstract: One of the goals of AI-based computational pathology is to generate compact representations of whole slide images (WSIs) that capture the essential information needed for diagnosis. While such approaches have been applied to histopathology, few ... ...

Abstract	One of the goals of AI-based computational pathology is to generate compact representations of whole slide images (WSIs) that capture the essential information needed for diagnosis. While such approaches have been applied to histopathology, few applications have been reported in cytology. Bone marrow aspirate cytology is the basis for key clinical decisions in hematology. However, visual inspection of aspirate specimens is a tedious and complex process subject to variation in interpretation, and hematopathology expertise is scarce. The ability to generate a compact representation of an aspirate specimen may form the basis for clinical decision-support tools in hematology. In this study, we leverage our previously published end-to-end AI-based system for counting and classifying cells from bone marrow aspirate WSIs, which enables the direct use of individual cells as inputs rather than WSI patches. We then construct bags of individual cell features from each WSI, and apply multiple instance learning to extract their vector representations. To evaluate the quality of our representations, we conducted WSI retrieval and classification tasks. Our results show that we achieved a mAP@10 of 0.58 ±0.02 in WSI-level image retrieval, surpassing the random-retrieval baseline of 0.39 ±0.1. Furthermore, we predicted five diagnostic labels for individual aspirate WSIs with a weighted-average F1 score of 0.57 ±0.03 using a k-nearest-neighbors (k-NN) model, outperforming guessing using empirical class prior probabilities (0.26 ±0.02). We present the first example of exploring trainable mechanisms to generate compact, slide-level representations in bone marrow cytology with deep learning. This method has the potential to summarize complex semantic information in WSIs toward improved diagnostics in hematology, and may eventually support AI-assisted computational pathology approaches.
Language	English
Publishing date	2023-10-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107530
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 653: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Proportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images.

Hosseini, S Maryam / Sikaroudi, Milad / Babaie, Morteza / Tizhoosh, H R

IEEE transactions on medical imaging

2023 Volume 42, Issue 7, Page(s) 1982–1995

Abstract: Medical centers and healthcare providers have concerns and hence restrictions around sharing data with external collaborators. Federated learning, as a privacy-preserving method, involves learning a site-independent model without having direct access to ... ...

Abstract	Medical centers and healthcare providers have concerns and hence restrictions around sharing data with external collaborators. Federated learning, as a privacy-preserving method, involves learning a site-independent model without having direct access to patient-sensitive data in a distributed collaborative fashion. The federated approach relies on decentralized data distribution from various hospitals and clinics. The collaboratively learned global model is supposed to have acceptable performance for the individual sites. However, existing methods focus on minimizing the average of the aggregated loss functions, leading to a biased model that performs perfectly for some hospitals while exhibiting undesirable performance for other sites. In this paper, we improve model "fairness" among participating hospitals by proposing a novel federated learning scheme called Proportionally Fair Federated Learning, short Prop-FFL. Prop-FFL is based on a novel optimization objective function to decrease the performance variations among participating hospitals. This function encourages a fair model, providing us with more uniform performance across participating hospitals. We validate the proposed Prop-FFL on two histopathology datasets as well as two general datasets to shed light on its inherent capabilities. The experimental results suggest promising performance in terms of learning speed, accuracy, and fairness.
MeSH term(s)	Humans ; Hospitals ; Supervised Machine Learning ; Pathology
Language	English
Publishing date	2023-06-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2023.3234450
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 2546: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

To top

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito