Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Quantitative Biology > Tissues and Organs

arXiv:2306.16989 (q-bio)
[Submitted on 29 Jun 2023]

Title:The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

Authors:Michael Robben, Amir Hajighasemi, Mohammad Sadegh Nasr, Jai Prakesh Veerla, Anne M. Alsup, Biraaj Rout, Helen H. Shang, Kelli Fowlds, Parisa Boodaghi Malidarreh, Paul Koomey, MD Jillur Rahman Saurav, Jacob M. Luber
View PDF
Abstract:Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight.
Subjects: Tissues and Organs (q-bio.TO); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
Cite as: arXiv:2306.16989 [q-bio.TO]
  (or arXiv:2306.16989v1 [q-bio.TO] for this version)
  https://doi.org/10.48550/arXiv.2306.16989
Journal reference: F1000Research 2023, 12:1436
Related DOI: https://doi.org/10.12688/f1000research.139210.1

Submission history

From: Jacob Luber [view email]
[v1] Thu, 29 Jun 2023 14:47:03 UTC (1,252 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
license icon view license
Current browse context:
q-bio.TO
< prev   |   next >
new | recent | 2023-06
Change to browse by:
cs
cs.CV
eess
eess.IV
q-bio

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)