scholarly journals From What to Why, the Growing Need for a Focus Shift Toward Explainability of AI in Digital Pathology

2022 ◽  
Vol 12 ◽  
Author(s):  
Samuel P. Border ◽  
Pinaki Sarder
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Digital Pathology ◽  
Model Bias ◽  
Biological Mechanisms ◽  
Additional Information ◽  
Specific Input ◽  
Minimal Work ◽  
Performance Gains ◽  
Made In

While it is impossible to deny the performance gains achieved through the incorporation of deep learning (DL) and other artificial intelligence (AI)-based techniques in pathology, minimal work has been done to answer the crucial question of why these algorithms predict what they predict. Tracing back classification decisions to specific input features allows for the quick identification of model bias as well as providing additional information toward understanding underlying biological mechanisms. In digital pathology, increasing the explainability of AI models would have the largest and most immediate impact for the image classification task. In this review, we detail some considerations that should be made in order to develop models with a focus on explainability.

scholarly journals Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

scholarly journals Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

2021 ◽  
Vol 11 (4) ◽  
pp. 280
Author(s):  
Andrea Termine ◽  
Carlo Fabrizio ◽  
Claudia Strafella ◽  
Valerio Caputo ◽  
Laura Petrosini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Neurodegenerative Diseases ◽  
Neurodegenerative Disorders ◽  
Drug Repurposing ◽  
Biomedical Data ◽  
Parkinson’S Diseases ◽  
Made In

In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases through the integration of all sources of biomedical data.

Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats

2021 ◽  
pp. 019262332098324 ◽  
Author(s):  
Hannah Pischon ◽  
David Mason ◽  
Bettina Lawrenz ◽  
Olivier Blanck ◽  
Anna-Lena Frisk ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Direct Detection ◽  
Digital Pathology ◽  
Specific Cell ◽  
Cytoplasmic Area ◽  
Quantitative Results ◽  
Hematoxylin And Eosin ◽  
Accuracy And Repeatability ◽  
First Time

Digital pathology evolved rapidly, enabling more systematic usage of image analysis and development of artificial intelligence (AI) applications. Here, combined AI models were developed to evaluate hepatocellular hypertrophy in rat liver, using commercial AI-based software on hematoxylin and eosin-stained whole slide images. In a first approach, deep learning-based identification of critical tissue zones (centrilobular, midzonal, and periportal) enabled evaluation of region-specific cell size. Mean cytoplasmic area of hepatocytes was calculated via several sequential algorithms including segmentation in microanatomical structures (separation of sinusoids and vessels from hepatocytes), nuclear detection, and area measurements. An increase in mean cytoplasmic area could be shown in groups given phenobarbital, known to induce hepatocellular hypertrophy when compared to control groups, in multiple studies. Quantitative results correlated with the gold standard: observation and grading performed by board-certified veterinary pathologists, liver weights, and gene expression. Furthermore, as a second approach, we introduce for the first time deep learning-based direct detection of hepatocellular hypertrophy with similar results. Cell hypertrophy is challenging to pick up, particularly in milder cases. Additional evaluation of mean cytoplasmic area or direct detection of hypertrophy, combined with histopathological observations and liver weights, is expected to increase accuracy and repeatability of diagnoses and grading by pathologists.

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

scholarly journals Detecting orientation of Brain MR scans using deep learning

2021 ◽  
Author(s):  
Chinmay Singhal ◽  
Nihit Gupta ◽  
Anouk Stein ◽  
Quan Zhou ◽  
Leon Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Free Text ◽  
Learning Models ◽  
Human Errors ◽  
Linguistic Barriers ◽  
Model Training ◽  
The Impact ◽  
Deep Learning Model ◽  
Made In

AbstractThere has been a steady escalation in the impact of Artificial Intelligence (AI) on Healthcare along with an increasing amount of progress being made in this field. While many entities are working on the development of significant deep learning models for the diagnosis of brain-related diseases, identifying precise images needed for model training and inference tasks is limited due to variation in DICOM fields which use free text to define things like series description, sequence and orientation [1]. Detecting the orientation of brain MR scans (Axial/Sagittal/Coronal) remains a challenge due to these variations caused by linguistic barriers, human errors and de-identification - essentially rendering the tags unreliable [2, 3, 4]. In this work, we propose a deep learning model that identifies the orientation of brain MR scans with near perfect accuracy.

scholarly journals PAI-WSIT: a Comprehensive Curated Resource for Cancerous Pathology With Deep Learning

2021 ◽  
Author(s):  
Changjiang Zhou ◽  
Xiaobing Feng ◽  
Yi Jin ◽  
Harvest F. Gu ◽  
Youcai Zhao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Data Collection ◽  
Function Analysis ◽  
Digital Pathology ◽  
Clinical Information ◽  
Improve Patient Care ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Region Detection

Abstract BackgroundThe possibility of digitizing whole-slide images (WSI) of tissue has led to the advent of artificial intelligence (AI) in digital pathology. Advances in precision oncology have resulted in an increasing demand for predictive assays that enable mining of subvisual morphometric phenotypes and might improve patient care ultimately. Hence, a pathologist-annotated and artificial intelligence-empowered platform for integration and analysis of WSI data and molecular detection data in tumors was established, called PAI-WSIT (http://www.paiwsit.com).MethodsThe standardized data collection process was used for data collection in PAI-WSIT, while a multifunctional annotation tool was developed and a user-friendly search engine and web interface were integrated for the database access. Furthermore, deep learning frameworks were applied in two tasks to detect malignant regions and classify phenotypic subtypes in colorectal cancers (CRCs), respectively.ResultsPAI-WSIT recorded 8633 WSIs of 1772 tumor cases, of which CRC from four regional hospitals in China and The Cancer Genome Atlas (TCGA) were the main ones, as well as cancers in breast, lung, prostate, bladder, and kidneys from two Chinese hospitals. A total of 1298 WSIs with high-quality annotations were evaluated by a panel of 8 pathologists. Gene detection reports of 582 tumor cases were collected. Clinical information of all tumor cases was documented. Besides, we reached overall accuracy of 0.933 in WSI classification for malignant region detection of CRC, and aera under the curves (AUC) of 0.719 on colorectal subtype dataset.ConclusionsCollectively, the annotation function, data integration and AI function analysis of PAI-WSIT provide support for AI-assisted tumor diagnosis, all of which have provided a comprehensive curation of carcinomas pathology.

A Deliberation on the Stages of Artificial Intelligence

2021 ◽  
Author(s):  
Jiran Kurian ◽  
Rohini V
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Being ◽  
Research Period ◽  
Actual Depth ◽  
General Standard ◽  
Made In

Artificial Intelligence (AI) is a technology that can be programmed to mimic humans' natural intelligence, which helps the machines perform the tasks that a human being can do. After a long research period from 1955, the researchers have achieved remarkable achievements like machine learning and deep learning in this field. Other areas like education, agriculture, medical, etc., to name a few, also utilizing these technologies for its improvements. All the achievements made in this field are not even comparable to the actual depth of this technology, where the depth of AI is yet to measure; that is, a long way to go to develop a fully functional AI. To identify the extent of its depth, firstly, the path to the AI's core should be visibly defined, and secondly, the milestones are to be placed in between. There are some general stages and types of AI introduced by other researchers, but it cannot be used for further research due to the inconsistency in the information. So, to bring standardized information in the Stages of AI is as important as setting up a good base in this field. The paper proposes and defines new stages of AI that could help set the milestones. The work also places a general standard, brings more clarity, and eliminates the inconsistencies in the Stages of AI.

scholarly journals Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1673 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Junya Fujimoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Diagnosis And Prognosis ◽  
Model Interpretation ◽  
Lung Cancer Diagnosis ◽  
Cancer Pathology ◽  
Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

scholarly journals Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 797 ◽  
Author(s):  
Hanadi El El Achi ◽  
Joseph D. Khoury
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Digital Images ◽  
Digital Pathology ◽  
Digital Microscopy ◽  
Clinical Environment ◽  
Lymphoproliferative Diseases ◽  
Glass Slides ◽  
Pathology Data ◽  
Whole Slide Images

Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

scholarly journals Developing Deep Learning Applications for Life Science and Pharma Industry

Drug Research ◽  
2018 ◽  
Vol 68 (06) ◽  
pp. 305-310 ◽  
Author(s):  
Daniel Siegismund ◽  
Vasily Tolkachev ◽  
Stephan Heyse ◽  
Beate Sick ◽  
Oliver Duerr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Life Science ◽  
Digital Pathology ◽  
Cost Effective ◽  
Automated Analysis ◽  
Complex Data ◽  
Research Activities ◽  
Human Labor ◽  
Effective Development

AbstractDeep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be ‘game changing’ for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning. A typical example is the automated analysis of images which are typically produced en-masse in many domains, e. g., in high-content screening or digital pathology. Deep learning enables to create competitive applications in so-far defined core domains of ‘human intelligence’. Applications of artificial intelligence have been enabled in recent years by (i) the massive availability of data samples, collected in pharma driven drug programs (=‘big data’) as well as (ii) deep learning algorithmic advancements and (iii) increase in compute power. Such applications are based on software frameworks with specific strengths and weaknesses. Here, we introduce typical applications and underlying frameworks for deep learning with a set of practical criteria for developing production ready solutions in life science and pharma research. Based on our own experience in successfully developing deep learning applications we provide suggestions and a baseline for selecting the most suited frameworks for a future-proof and cost-effective development.

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