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Bioengineering
Special Issues
Computational Pathology and Artificial Intelligence
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Computational Pathology and Artificial Intelligence

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 2305

Special Issue Editor

Dr. Zeynettin Akkus

E-Mail Website
Guest Editor
Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
Interests: digital pathology; histopathological image analysis; imaging informatics; artificial intelligence; cloud computing

Special Issue Information

Dear Colleagues,

With the advancement of digital pathology and artificial intelligence (AI), computational pathology and AI emerges as an interdisciplinary field that combines pathology and computer science for the analysis of histopathological images. Computational pathology and AI aims to leverage digital pathology slides, clinical and molecular data along with deep learning techniques to enhance the accuracy, efficiency, and scalability of diagnostic and prognostic processes. Computational pathology and AI holds immense promise and potential benefits in the future of lab medicine and pathology.

Our computational pathology and AI Special Issue will focus on AI applications in computational pathology including automated diagnosis, predicting patient outcomes, tumor classification, image segmentation, drug discovery and development, and laboratory workflow optimization. This exciting and comprehensive Special Issue will shed light on significant advancements in this field and its potential impact on healthcare.

Dr. Zeynettin Akkus
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational pathology
  • artificial intelligence
  • deep learning
  • histopathological image analysis
  • digital pathology
  • digital microscope
  • pathology workflow
  • drug discovery and development

Published Papers (2 papers)

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Research

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14 pages, 7317 KiB  
Article
Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model
by Miguel Luna, Philip Chikontwe and Sang Hyun Park
Bioengineering 2024, 11(3), 294; https://doi.org/10.3390/bioengineering11030294 - 21 Mar 2024
Viewed by 857
Abstract
Segmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM) can help improve the detection quality of rare types [...] Read more.
Segmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM) can help improve the detection quality of rare types of nuclei. In this work, we introduce category descriptors to perform nuclei segmentation and classification by prompting the SAM model. We close the domain gap between histopathology and natural scene images by aligning features in low-level space while preserving the high-level representations of SAM. We performed extensive experiments on the Lizard dataset, validating the ability of our model to perform automatic nuclei segmentation and classification, especially for rare nuclei types, where achieved a significant detection improvement in the F1 score of up to 12%. Our model also maintains compatibility with manual point prompts for interactive refinement during inference without requiring any additional training. Full article
(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)
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Review

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13 pages, 1237 KiB  
Review
Applications of Large Language Models in Pathology
by Jerome Cheng
Bioengineering 2024, 11(4), 342; https://doi.org/10.3390/bioengineering11040342 - 31 Mar 2024
Cited by 1 | Viewed by 995
Abstract
Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with [...] Read more.
Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with vision models can assist in interpreting histopathology images. LLMs have immense potential in transforming pathology practice and education, but these models are not infallible, so any artificial intelligence generated content must be verified with reputable sources. Caution must be exercised on how these models are integrated into clinical practice, as these models can produce hallucinations and incorrect results, and an over-reliance on artificial intelligence may lead to de-skilling and automation bias. This review paper provides a brief history of LLMs and highlights several use cases for LLMs in the field of pathology. Full article
(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)
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