Category: Image recognition and understanding

Image recognition and understanding

AI-based diagnostic support in medicine with »Pneumo.AI«

Advantages

  • Creativity & Innovation
  • Energy efficiency
  • Expense & Revenue Optimization
  • Knowledge & Complexity reduction
  • Material efficiency
  • Precision & Quality
  • Process optimization
  • Real-time results 24/7
  • Time efficiency / Agiltiy
  • Workload relief / Comfort

Functional areas

  • Process optimization

To the demonstrator

→ Go to the interactive AI game »Pneumo.AI«
To overview

AI-based diagnostic support in medicine with »Pneumo.AI«

»Pneumo.AI« is an AI-supported diagnostic software which supports medical professionals in the accurate identification of pneumonia using intelligent image recognition.

In which areas does the AI application offer the greatest benefit?

The »Pneumo.AI« demonstrator was developed to playfully illustrate the ways AI technologies and medical professionals can work together. The disease pneumonia, better known as lung inflammation, keeps specialized medical specialists busy in many hospitals. Until now, possible diseases had to be identified manually using X-ray images, among other things, to initiate appropriate treatment in good time. Today, image recognition systems can help with diagnosis. This not only saves time but can also prevent misjudgments.

What is pneumonia?

Pneumonia is an acute inflammation of the lower respiratory tract, commonly referred to as lung inflammation. Contrary to current belief, pneumonia is still a serious disease in many regions of the world today. In developing countries in particular, pneumonia is one of the most common causes of illness and death in children under the age of five.

How can AI help with diagnosis?

Artificial intelligence systems can support doctors in making a diagnosis. In this case, computer vision, i.e., machine vision, helps to recognize disease characteristics on chest scans.

What is the future of AI in medicine?

Both doctors and data scientists alike see great potential for AI in medicine. Many hospitals have large amounts of data available which could be used to improve diagnostic support. However, it is important that AI systems are only ever understood as assistance tools and that medical staff always remain in charge of decision-making. Also, medical data is highly sensitive and requires special protection.

What are »Pneumo.AI’s« quality characteristics?

  • Low effort: Since the annotation of medical image datasets usually involves a great deal of effort, it is important to develop data-efficient algorithms to achieve the lowest possible annotation effort.
  • Immediate analysis: The use of AI technologies allows for immediate evaluation of the scan/X-ray image after it has been taken – with no human interaction. This shows the potential to optimize work processes in clinics, for instance by developing a prioritization system. However, it is important that AI always serves as an assistance system for doctors and never makes decisions on its own.  
  • Secure data processing: For sensitive data such as patient data, it is essential that the AI processes used are secure. All data must be stored on German servers or may only be processed locally by medical specialists or in hospitals. 
  • Powerful AI: In the future, AI-based multimodal analysis will also play an important role in the evaluation of medical image data, as doctors will have a wide range of information available to them during the diagnostic process regarding the patient’s health status or the course of the disease.
»Close collaboration between medical experts and data scientists is the most important basis for the use of artificial intelligence in medicine.«
Helen Schneider
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

Which AI technology is used in the KI.NRW demonstrator?

Deep Learning

The Pneumo.AI demonstrator is based on deep convolutional neural networks (CNNs), which are particularly well suited for processing large image data sets. This technology can also be transferred to other diseases and use cases.

Informed Machine Learning

In this type of machine learning, available prior knowledge and expert knowledge are integrated into the model to develop more data-efficient algorithms, for instance. For Pneumo.AI, the elements of bilateral symmetry of the lung field were considered within the modeling.

Computer Vision

To ensure a good generalization capability of the trained network, various augmentation techniques are implemented. By rotating and zooming the training image data, the network achieves better performance and overfitting is avoided.

What does the AI demonstrator show?

The »Pneumo.AI« demonstrator shows how AI technologies can support doctors in practices and clinics in their everyday work in the future. It is important to emphasize that artificial intelligence is available to medical professionals as an assistance tool, but that the final decision remains with the human being. The demonstrator also illustrates the great potential of AI in medical image processing.

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Curious? Click here to go straight to the demonstrator!

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Where can I find more information?

AI.Map featuring entries from the medical field

More AI providers, applications, and AI products »made in NRW« with the same AI focus can be found using the filter and search function of the AI.Map, which currently contains more than 1000 entries.

Go to the AI.Map

Lecture on Pneumo.AI at the MEDICA 2022 trade fair

At the international medical trade fair MEDICA 2022, KI.NRW and Fraunhofer IAIS gave a presentation on »Artificial intelligence in healthcare using the example of Pneumo.AI«.

Go to the lecture

SmartHospital: The use of AI in the hospital of the future

The KI.NRW flagship project SmartHospital.NRW aims at developing tools to support hospitals in their digital transformation and in the use of AI. Determine your hospital’s AI maturity level now.

Go to the AI Readiness Check

Contact the team of developers

Helen Schneider

Data Scientist – Computer Vision

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 14-2735

Send email

Dr. Rafet Sifa

Head of Cognitive Business Optimization

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 14-2405

Send email

Image recognition and understanding

Image-based quality control “Damage Detection”

Advantages

  • Precision & Quality
  • Relief & Comfort
  • Time efficiency & Agility

Functional areas

  • Production / Assembly
  • Storage / Logistics

To the demonstrator

→ To the movie about "Damage Detection"
To overview

Image-based quality control “Damage Detection”

Automated inspection of damage and defects using AI-assisted quality control of reflective surfaces.

Where does the AI application offer the greatest benefit?

The inspection of reflective surfaces, for example of quality defects in production or damage to vehicles, is often still carried out manually and therefore turns out to be time-consuming and requires a high level of expertise on the part of the persons in charge. In order to increase the productivity of the processes, “Damage Detection”, an AI system for the quality control of shiny or diffusely reflecting surfaces, has been developed. It is suitable for a wide range of applications: from industrial production, for example in the automotive industry, to the leasing and insurance industry and to automotive appraisals. The system works automatically and takes less than a minute for surface inspection. The quality defects found are categorized using Deep Learning. The combination of deflectometry, i.e. the contact-free detection of reflective surfaces, conventional image recognition processes and artificial intelligence methods is a unique system.

What are the quality indicators of such AI applications?

  • “Damage Detection” offers low hardware and maintenance costs as a solution. The AI application can be used flexibly and retrofitted into existing productions. Among other things, the solution is able to work under the influence of stray light (e.g. ceiling lighting in a production hall).
  • The system delivers 100% test coverage. It combines high accuracy with low hardware requirements. Currently, defects as small as 0.1 mm can be detected on 1 m component size.
  • By using Artificial Intelligence, the system can be trained to detect various types of defects on the surface.
  • In addition to highly reflective surfaces, the latest developments also allow diffusely reflective surfaces to be tested for their quality.
“The combination of conventional image recognition
methods, AI methods and the detection of reflective
surfaces is unique.”
Dr. rer. nat. Theresa Bick
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

What AI technology powers the demonstrator of KI.NRW?

Computer Vision

The solution relies on Convolutional Neural Networks (CNNs) for detection and classification of the surface anomalies. The CNN model belongs to the Artificial Neural Networks, which are suitable for image recognition and understanding.

Informed Machine Learning

By incorporating expert knowledge, the system is able to make dataintensive neural networks practical for industrial production. The amount of training data and the annotation effort can be kept comparatively low.

Combination with classical image processing

The AI is linked to classical image processing. The system uses the advantages of both worlds – fast, approximate algorithms from classical image processing and the powerful methods of deep learning.

What does the AI demonstrator show?

Quality control of shiny or diffusely reflecting surfaces is notable for its simple, mobile hardware design. It operates independently of ambient light and is fully automated. It takes less than one minute for a surface inspection.

Where is more information to be found?

Image recognition in practice

Many “good practice examples” as inspiration for the possible applications of this AI.

To the examples

AI products “made in NRW”

Filter our AI map by “image recognition and understanding”.

To the AI map

AI provider from NRW

Our AI map illustrates who offers AI methods related to image recognition in their portfolio.

To the AI map

Contact us

Dr.-Ing. Stefan Eickeler

Senior Research Engineer

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 141969

E-Mail senden

Image recognition and understanding

Intelligent document analysis “recognAIze”

Advantages

  • Precision & Quality
  • Time efficiency & Agility

Functional areas

  • Assistance / Support / Staff positions
  • Distribution / Sales
  • Finances / Law
  • Human Resources
  • IT / Infrastructure
  • Management / Project Management
  • Marketing / Sales
  • Production / Assembly
  • Purchasing / Procurement
  • Research / Development
  • Storage / Logistics
  • Support / Customer service
  • Web design / Communication

To the demonstrator

→ To recognAIze
To overview
Image recognition and understanding

Intelligent document analysis “recognAIze”

With the intelligent document analysis of recognAIze, data from documents can be recognized and evaluated automatically.

Where does the AI application offer the greatest benefit?

The manual review of receipts, invoices and other documents, their digital capture and provision is associated with a high expenditure of time and money in many companies as well as administrative institutions. The solution is provided by intelligent document analysis systems based on optical character recognition (OCR), which, like “recognAIze”, enable fast, simple and automated analysis as well as blind processing of all types of documents. Thanks to artificial intelligence, documents are automatically captured, read, assigned and further processed. Damaged originals, low-quality scans of documents and, in particular, confidential documents are processed without further human intervention and according to high data protection standards.

What are the quality indicators in these types of AI applications?

  • The basis of document analysis is the input data that needs to be analyzed. Since the documents are usually captured in varying image quality, automated image enhancement is very important in the AI application.
  • AI-based optical character recognition (OCR) using artificial neural networks ensures that not only individual text characters are recognized and processed, but also text passages and the structure of a document (e.g. headers or footnotes).
  • Through layout analysis, the AI application can also identify tables within a document and interpret the contents to process invoices automatically in accounting, for example.
  • Particularly in the case of sensitive information, the AI used must be secure and all data must be processed in a DSGVO-compliant manner on German servers or on-premise at the customer’s premises.
  • In the future, handwriting recognition (ICR) will also play a role in the applications in order to open up additional fields of application and achieve a complete transfer of content.

What AI technology powers demonstrator of KI.NRW?

Deep Learning OCR

Optical Character Recognition (OCR) combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), two current approaches in the field of Artificial Intelligence, to develop characters from pixels. It extracts the text from the images and generates a structured XML file for each document with position data of the recognized words and page ranges.

Image enhancement

For the best possible quality of the results, negative influencing factors such as insufficient exposure of the scanned document or curvature or distortion in the image must be equalized. The image enhancement algorithms perform grayscale conversion and binarization for this purpose. In addition, procedures are used to remove curvature and other disturbing factors.

Layout recognition

The layout recognition identifies the structure of text and helps to divide the recognized characters into columns, text sections or headings and to determine a reading order. In this way, table structures can also be recognized and output again as such, e.g. as a csv file. The output format is provided with appropriate metadata.

“Thanks to the methods used for image enhancement, layout and
character recognition, even poor quality documents can be evaluated.”
Dr. Nicolas Flores-Herr
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

What does the AI demonstrator show?

The KI.NRW demonstrator “recognAIze” brings AI-supported document analysis to life. Via the application, your own photographed or scanned documents can be uploaded to the system, where they are available for testing the intelligent document analysis. The step by step animations guide you through the AI technologies used in the demonstrator.

Optimize documents

Often, photographed or scanned documents have a fluctuating image quality, and are sometimes bumpy, torn or dirty. Image enhancement processes ensure that even old or damaged documents can be processed. The “recognAIze” demonstrator vividly guides you through the range of optimization options that are essential for high-quality document analysis.

Recognize characters and structures

The accuracy and speed of the OCR engine for intelligent character recognition from “recognAIze” is higher than that of leading market players. Without templates and manual post-processing, the demonstrator recognizes document layouts, e.g. sender information or dates. Even complex text content such as text-around-image elements are reliably recognized by the application.

Understand tables

Tables pose a particular challenge because they can be structured differently from document to document. AI methods are responsible for subdividing table contents according to information types and interpreting the segments individually.

Classify content

The demonstrator “recognAIze” determines the properties of the document, evaluates the individual elements and thereby enables a whole range of subsequent processing. For example, the intelligent classification makes blind processing of confidential documents possible in the first place. This means that information can be aggregated or used pseudonymously without a human being having access to the documents. In this way, sensitive, personal data can be protected better.

Create interfaces

AI-supported document analysis is often at the beginning of a process chain, whether in accounting or in archives. To enable further processing steps, the KI.NRW demonstrator offers various output formats such as XML or PDF.

Are you curious?
Click here to go to the demonstrator!

Try it now

Where is more information to be found?

Study by KI.NRW

Learn about where we encounter modern language technologies in our everyday and professional lives and the economic opportunities (only available in German language).

To the Study

AI products “Made in NRW”

Filter our AI map by “language and text comprehension”.

To the AI map

AI provider from NRW

Our AI map illustrates who offers AI methods related to image recognition in their portfolio.

To the AI map

Contact us

Dr. Nicolas Flores-Herr

Business Unit Manager Document Analytics

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 142532

Email schreiben

Dr. Iuliu Konya

Senior Research Engineer

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 142543

Email schreiben

 

Marius Nißlmüller

Student assistant Business Development

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Email schreiben

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