ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   04/24/2019
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The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
No. 44 ID: Use Case Name: Chromosome Segmentation and Deep Classification
Application
Domain
Healthcare
Deployment
Model
Hybrid or other
StatusPoC
Scope
Objective(s)
  • Automating Karyotyping of the chromosomes in cell spread images.
  • Segmentation of chromosomes in the images using non expert crowd
Short
Description
(up to
150 words)
Karyotyping of the chromosomes micro-photographed under metaphase is done by characterizing the individual chromosomes in cell spread images. Currently, considerable effort and time is spent to manually segment out chromosomes from cell images, and classifying the segmented chromosomes. We proposed a method to segment out and classify chromosomes for healthy patients using a combination of crowdsourcing, preprocessing and deep learning, wherein the non-expert crowd from external crowdsourcing platform is utilized to segment out the chromosomes, which are then classified using deep neural network. Results are encouraging and promise to significantly reduce the cognitive burden of segmenting and karyotyping chromosomes.
Complete Description Metaphase chromosome analysis is one of the primary techniques utilized in cytogenetics. Observations of chromosomal segments or translocations during metaphase can indicate structural changes in the cell genome, and is often used for diagnostic purposes. Karyotyping of the chromosomes micro-photographed under metaphase is done by characterizing the individual chromosomes in cell spread images. Currently, considerable effort and time is spent to manually segment out chromosomes from cell images, and classifying the segmented chromosomes into one of the 24 types, or for diseased cells to one of the known translocated types. Segmenting out the chromosomes in such images can be especially laborious and is often done manually, if there are overlapping chromosomes in the image which are not easily separable by image processing techniques. Many techniques have been proposed to automate the segmentation and classification of chromosomes from spread images with reasonable accuracy, but given the criticality of the domain, a human in the loop is often still required. In this paper, we present a method to segment out and classify chromosomes for healthy patients using a combination of crowdsourcing, preprocessing and deep learning, wherein the non-expert crowd from CrowdFlower is utilized to segment out the chromosomes from the cell image, which are then straightened and fed into a (hierarchical) deep neural network for classification. Experiments are performed on 400 real healthy patient images obtained from a hospital. Results are encouraging and promise to significantly reduce the cognitive burden of segmenting and karyotyping chromosomes.
Stakeholders
Stakeholders'
Assets,Values
Systems'
Threats &
Vulnerabilities
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 Classifier Accuracy Without straightening and pre-processing, the average classification accuracy obtained was 68.5%. However, with preprocessing, the classification accuracy improved to 86.7%. These results are very likely to improve with more annotated training data for classification.
2 Annotation Completeness 35.9 chromosomes segmented out after crowd annotation, for 50 images having 46 chromosomes
AI Features Task(s)Recognition
Method(s)
Hardware
Topology
Terms &
Concepts Used
Deep learning, crowd sourcing, non-expert crowd, segmentation, karyotyping
Standardization
Opportunities
Requirements
Challenges
& Issues
  • Crowds job satisfaction
  • Spamming in annotated data
Societal Concerns Description
SDGs to
be achieved
Good health and well-being for people
Data Characteristics
Description The dataset comprised of 400 stained images with varying degrees of overlap between chromosomes, out of which 200 were kept for testing and the remaining for training and validation
Source Partner hospital
Type Images
Volume (size) 400
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name
Scenario
Description
Triggering Event Pre-condition Post-Condition






Scenario Name Training
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case
Originator
Organization
Link
1 Sharma, Monika & Saha, Oindrila & Sriraman, Anand & Hebbalaguppe, Ramya & Vig, Lovekesh & Karande, Shirish. (2017). Crowdsourcing for Chromosome Segmentation and Deep Classification. 786-793. 10.1109/CVPRW.2017.109. Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013