scholarly journals Pathology Image Exchange: The Dutch Digital Pathology Platform for Exchange of Whole-Slide Images for Efficient Teleconsultation, Telerevision, and Virtual Expert Panels

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Paul J. van Diest ◽  
André Huisman ◽  
Jaap van Ekris ◽  
Jos Meijer ◽  
Stefan Willems ◽  
...  
Keyword(s):  
Virtual Slide ◽  
Digital Pathology ◽  
Real Life ◽  
Patient Privacy ◽  
Remote Consultation ◽  
Technical Validation ◽  
Virtual Expert ◽  
The Many ◽  
Pathology Image ◽  
Whole Slide Images

Among the many uses of digital pathology, remote consultation, remote revision, and virtual slide panels may be the most important ones. This requires basic slide scanner infrastructure in participating laboratories to produce whole-slide images. More importantly, a software platform is needed for exchange of these images and functionality to support the processes around discussing and reporting on these images without breaching patient privacy. This poses high demands on the setup of such a platform, given the inherent complexity of the handling of digital pathology images. In this article, we describe the setup and validation of the Pathology Image Exchange project, which aimed to create a vendor-independent platform for exchange of whole-slide images between Dutch pathology laboratories to facilitate efficient teleconsultation, telerevision, and virtual slide panels. Pathology Image Exchange was released in April 2018 after technical validation, and a first successful validation in real life has been performed for hematopathology cases.

Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

2020 ◽  
Vol 2020 (10) ◽  
pp. 64-1-64-5
Author(s):  
Mustafa I. Jaber ◽  
Christopher W. Szeto ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Stephen C. Benz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Density ◽  
Majority Voting ◽  
Training Set ◽  
Density Maps ◽  
Color Deconvolution ◽  
Map Generation ◽  
Density Map ◽  
Pathology Image ◽  
Whole Slide Images

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

KEARIFAN LOKAL (LOCAL GENIUS) SEBAGAI SOKO GURU MENATA PERADABAN BANGSA YANG BERKARAKTER NUSANTARA (REFLEKSI KARYA KI DALANG TANGSUB)

2016 ◽  
Vol 2 (12) ◽  
Author(s):  
I Made Suarta
Keyword(s):  
Lifelong Learning ◽  
Local Knowledge ◽  
Real Life ◽  
Negative Attitudes ◽  
Care Environment ◽  
Mental Attitude ◽  
Early 19Th Century ◽  
The People ◽  
The Many ◽  
Written Traditions

Local knowledge (local genius) is the quintessence of our ancestors thinking either oral or written traditions which we have received to date. Thought that, in the context of real archipelago has the same thread, which has a valuable values and universal to strengthen the integrity of the Unitary Republic of Indonesia. Through our founding genius thought that we should be able to implement it in real life to be able to reach people who "Gemah ripah loh jinawi", no less clothing, food, and shelter!Some of the many concepts of mind for the people of Bali are reflected in the work of puppeteer Ki Dalang Tangsub contributed to the development of Indonesia and has a universal value is the concept of maintaining the environment, save money, and humble. Through mental attitude has not always feel pretty; like not smart enough, not skilled enough, and not mature enough experience, make us always learn and practice. Learn and continue lifelong learning will make a man more mature and a lot of experience. Thus, the challenges in life will be easy to overcome. All that will be achieved, in addition to the hard work is also based on the mental attitude of inferiority is not proud, haughty, arrogant and other negative attitudes. Thought care environment, managing finances, and humble as described above, in Bali has been formulated through a literature shaped geguritan, namely Geguritan I Gedé Basur Dalang Tangsub works, one of the great authors in the early 19th century.  Keywords: Local knowledge, a cornerstone of, the character of the archipelago

scholarly journals A Digital Pathology Solution to Resolve the Tissue Floater Conundrum

2020 ◽  
Author(s):  
Liron Pantanowitz ◽  
Pamela Michelow ◽  
Scott Hazelhurst ◽  
Shivam Kalra ◽  
Charles Choi ◽  
...  
Keyword(s):  
Digital Pathology ◽  
Primary Diagnosis ◽  
Image Search ◽  
Molecular Tests ◽  
Tissue Identification ◽  
Digital Database ◽  
Glass Slides ◽  
Search Tool ◽  
Hematoxylin And Eosin ◽  
Whole Slide Images

Context.— Pathologists may encounter extraneous pieces of tissue (tissue floaters) on glass slides because of specimen cross-contamination. Troubleshooting this problem, including performing molecular tests for tissue identification if available, is time consuming and often does not satisfactorily resolve the problem. Objective.— To demonstrate the feasibility of using an image search tool to resolve the tissue floater conundrum. Design.— A glass slide was produced containing 2 separate hematoxylin and eosin (H&E)-stained tissue floaters. This fabricated slide was digitized along with the 2 slides containing the original tumors used to create these floaters. These slides were then embedded into a dataset of 2325 whole slide images comprising a wide variety of H&E stained diagnostic entities. Digital slides were broken up into patches and the patch features converted into barcodes for indexing and easy retrieval. A deep learning-based image search tool was employed to extract features from patches via barcodes, hence enabling image matching to each tissue floater. Results.— There was a very high likelihood of finding a correct tumor match for the queried tissue floater when searching the digital database. Search results repeatedly yielded a correct match within the top 3 retrieved images. The retrieval accuracy improved when greater proportions of the floater were selected. The time to run a search was completed within several milliseconds. Conclusions.— Using an image search tool offers pathologists an additional method to rapidly resolve the tissue floater conundrum, especially for those laboratories that have transitioned to going fully digital for primary diagnosis.

scholarly journals Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Emanuela Paladini ◽  
Edoardo Vantaggiato ◽  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Texture Features ◽  
Tissue Type ◽  
Cancer Tissue ◽  
Learning Methods ◽  
Feature Based ◽  
Type Classification ◽  
Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

scholarly journals A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study

2020 ◽  
Vol 12 ◽  
pp. 175883592097141
Author(s):  
Fan Zhang ◽  
Lian-Zhen Zhong ◽  
Xun Zhao ◽  
Di Dong ◽  
Ji-Jin Yao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Nasopharyngeal Carcinoma ◽  
Treatment Failure ◽  
Digital Pathology ◽  
Imaging Features ◽  
Multi Scale ◽  
External Test ◽  
Whole Slide Images ◽  
Independent Cohort

Background: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). Methods: We recruited 220 NPC patients and divided them into training ( n = 132), internal test ( n = 44), and external test ( n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort). Results: Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689–0.779, all p < 0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the training (C-index: 0.817 versus 0.730, p < 0.050), internal test (C-index: 0.828 versus 0.602, p < 0.050) and external test (C-index: 0.834 versus 0.679, p < 0.050) cohorts. Furthermore, patients were stratified successfully into two groups with distinguishable prognosis (log-rank p < 0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort. Conclusion: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC.

scholarly journals Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks

2019 ◽  
Author(s):  
Seda Bilaloglu ◽  
Joyce Wu ◽  
Eduardo Fierro ◽  
Raul Delgado Sanchez ◽  
Paolo Santiago Ocampo ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Classification Problem ◽  
Classification Performance ◽  
Neoplastic Tissue ◽  
Multiple Tumor ◽  
Slide Image ◽  
Prediction Systems ◽  
Multi Class Classification ◽  
The Many ◽  
Whole Slide Images

AbstractVisual analysis of solid tissue mounted on glass slides is currently the primary method used by pathologists for determining the stage, type and subtypes of cancer. Although whole slide images are usually large (10s to 100s thousands pixels wide), an exhaustive though time-consuming assessment is necessary to reduce the risk of misdiagnosis. In an effort to address the many diagnostic challenges faced by trained experts, recent research has been focused on developing automatic prediction systems for this multi-class classification problem. Typically, complex convolutional neural network (CNN) architectures, such as Google’s Inception, are used to tackle this problem. Here, we introduce a greatly simplified CNN architecture, PathCNN, which allows for more efficient use of computational resources and better classification performance. Using this improved architecture, we trained simultaneously on whole-slide images from multiple tumor sites and corresponding non-neoplastic tissue. Dimensionality reduction analysis of the weights of the last layer of the network capture groups of images that faithfully represent the different types of cancer, highlighting at the same time differences in staining and capturing outliers, artifacts and misclassification errors. Our code is available online at: https://github.com/sedab/PathCNN.

Ovarian Frozen Section Diagnosis: Use of Whole-Slide Imaging Shows Excellent Correlation Between Virtual Slide and Original Interpretations in a Large Series of Cases

2010 ◽  
Vol 134 (7) ◽  
pp. 1020-1023 ◽  
Author(s):  
Margaret A. Fallon ◽  
David C. Wilbur ◽  
Manju Prasad
Keyword(s):  
Frozen Section ◽  
Virtual Slide ◽  
Granulosa Cell Tumor ◽  
Routine Practice ◽  
Corpora Lutea ◽  
Operating Characteristics ◽  
Frozen Section Diagnosis ◽  
Very High ◽  
Whole Slide Images

Abstract Context.—Whole-slide images (WSI) are a tool for remote interpretation, archiving, and teaching. Ovarian frozen sections (FS) are common and hence determination of the operating characteristics of the interpretation of these specimens using WSI is important. Objectives.—To test the reproducibility and accuracy of ovarian FS interpretation using WSI, as compared with routine analog interpretation, to understand the technology limits and unique interpretive pitfalls. Design.—A sequential series of ovarian FS slides, representative of routine practice, were converted to WSI. Whole-slide images were examined by 2 pathologists, masked to all prior results. Correlation characteristics among the WSI, the original, and the final interpretations were analyzed. Results.—A total of 52 cases, consisting of 71 FS slides, were included; 34 cases (65%) were benign, and 18 cases (35%) were malignant, borderline, and of uncertain potential (9 [17%], 7 [13%], and 2 [4%] of 52 cases, respectively). The correlation between WSI and FS interpretations was 96% (50 of 52) for each pathologist for benign versus malignant, borderline, and uncertain entities. Each pathologist undercalled 2 borderline malignant cases (4%) as benign cysts on WSI. There were no overcalls of benign cases. Specific issues within the benign and malignant groups involved endometriosis versus hemorrhagic corpora lutea, and granulosa cell tumor versus carcinoma, respectively. Conclusions.—The correlation between original FS and WSI interpretations was very high. The few discordant cases represent recognized differential diagnostic issues. Ability to examine gross pathology and real-time consultation with surgeons might be expected to improve performance. Ovarian FS diagnosis by WSI is accurate and reproducible, and thus, remote interpretation, teaching, and digital archiving of ovarian FS specimens by this method can be reliable.

Technological Innovations to Enhance Neurocognitive Rehabilitation

2017 ◽  
Author(s):  
Anthony J.-W. Chen ◽  
Fred Loya
Keyword(s):  
Brain Injury ◽  
Real Life ◽  
Intervention Development ◽  
Neurocognitive Functioning ◽  
Cognitive Changes ◽  
Rehabilitation Care ◽  
Neuroscience Research ◽  
Neurocognitive Rehabilitation ◽  
Neurocognitive Dysfunctions ◽  
The Many

In an instant, a brain injury can cause changes that affect a person for a life­time. Although traumatic brain injury (TBI) can result in almost any neurological deficit, the most common and persistent deficits tend to affect neurocognitive functioning. Functional issues may produce a tremendous chronic burden on individuals, families, and healthcare systems (Thurman, Alverson, Dunn, Guerrero, & Sniezek, 1999; Yu et al., 2003). The far-reaching impact of these seemingly “invisible” deficits is often not recognized. Individuals who have suffered a TBI may also be at increased risk for developing cognitive changes later in life (Mauri et al., 2006; Schwartz, 2009; Van Den Heuvel, Thornton, & Vink, 2007). Military veterans report even higher rates of persistent issues, especially in the context of posttraumatic stress (PTS) (Polusny et al., 2011). Despite their importance, chronic neurocognitive dysfunctions are often poorly addressed. A long-term view on care-oriented research and development is needed (Chen & D’Esposito, 2010). Even as we get deeper into the 21st century, there continue to be many gaps in the rehabilitation of neurocognitive functioning after brain injury. There is a need for increased effort to advance rehabilitation care and delivery. There are two major gaps in care that could benefit from neuroscience research and technology-assisted intervention development. First, there remains a major need for theory-driven approaches to cognitive training, accompanied by the development of innovative tools to support learning of useful skills and their generalization to help achieve real-life goals. Second, major gaps in the delivery and coordination of rehabilitation must be addressed in order to provide care to the many people with brain injury who lack access to services due to barriers imposed by distance, financial constraints, and disability. This chapter introduces and illustrates some technology-assisted innovations that may help to advance neurocognitive rehabilitation care. Examples of using technology to reach into the community via tele-rehabilitation, as well as exam­ples of reaching students in a manner aligned with their scholastic goals, are discussed.

Framework Design and Case Study for Privacy-Preserving Medical Data Publishing

2013 ◽  
Vol 4 (4) ◽  
pp. 48-65 ◽  
Author(s):  
Yu Niu ◽  
Ji-Jiang Yang ◽  
Qing Wang
Keyword(s):  
Data Sharing ◽  
Personal Information ◽  
Real Life ◽  
Privacy Preserving ◽  
Medical Data ◽  
Data Publishing ◽  
Free Text ◽  
Patient Privacy ◽  
Healthcare Data

With the pervasive using of Electronic Medical Records (EMR) and telemedicine technologies, more and more digital healthcare data are accumulated from multiple sources. As healthcare data is valuable for both commercial and scientific research, the demand of sharing healthcare data has been growing rapidly. Nevertheless, health care data normally contains a large amount of personal information, and sharing them directly would bring huge threaten to the patient privacy. This paper proposes a privacy preserving framework for medical data sharing with the view of practical application. The framework focuses on three key issues of privacy protection during the data sharing, which are privacy definition/detection, privacy policy management, and privacy preserving data publishing. A case study for Chinese Electronic Medical Record (ERM) publishing with privacy preserving is implemented based on the proposed framework. Specific Chinese free text EMR segmentation, Protected Health Information (PHI) extraction, and K-anonymity PHI anonymous algorithms are proposed in each component. The real-life data from hospitals are used to evaluate the performance of the proposed framework and system.

scholarly journals Seeing the Dog: Naturalistic Canine Representations from Greek Art

Arts ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Kenneth F. Kitchell
Keyword(s):  
Real Life ◽  
Ancient Greek ◽  
Seminal Work ◽  
Greek Art ◽  
The Many ◽  
Animal World

This study attempts to demonstrate that ancient Greek authors and vase painters (mostly of the late sixth and early fifth centuries) were well attuned to the many bodily gestures and positions exhibited by dogs in real life and utilized this knowledge in producing their works. Once this is clear, it becomes evident that the Greek public at large was equally aware of such canine bodily gestures and positions. This extends the seminal work on gestures of Boegehold and Lateiner to the animal world and seeks also to serve as a call for further study of similar animals throughout ancient Greek times.

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