Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis

• URL: http://arxiv.org/abs/2307.05519v1
• Date: Fri, 7 Jul 2023 12:02:54 GMT
• Title: Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
• Authors: Xiaoyi Ji, Richard Salmon, Nita Mulliqi, Umair Khan, Yinxi Wang, Anders Blilie, Henrik Olsson, Bodil Ginnerup Pedersen, Karina Dalsgaard S{\o}rensen, Benedicte Parm Ulh{\o}i, Svein R Kjosavik, Emilius AM Janssen, Mattias Rantalainen, Lars Egevad, Pekka Ruusuvuori, Martin Eklund, Kimmo Kartasalo
• Abstract summary: The potential of artificial intelligence in digital pathology is limited by technical inconsistencies in the production of whole slide images. Physical color calibration of scanners can standardize WSI appearance and enable robust AI performance.
• Score: 2.5088131681242483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical. Changes in the imaging workflow can also lead to compromised diagnoses and patient safety risks. We evaluated whether physical color calibration of scanners can standardize WSI appearance and enable robust AI performance. We employed a color calibration slide in four different laboratories and evaluated its impact on the performance of an AI system for prostate cancer diagnosis on 1,161 WSIs. Color standardization resulted in consistently improved AI model calibration and significant improvements in Gleason grading performance. The study demonstrates that physical color calibration provides a potential solution to the variation introduced by different scanners, making AI-based cancer diagnostics more reliable and applicable in clinical settings.

Related papers

• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency [2.063403009505468]
  Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. Current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. We propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to
  arXiv  Detail & Related papers  (2024-10-01T21:02:16Z)
• Deep intra-operative illumination calibration of hyperspectral cameras [73.08443963791343]
  Hyperspectral imaging (HSI) is emerging as a promising novel imaging modality with various potential surgical applications. We show that dynamically changing lighting conditions in the operating room dramatically affect the performance of HSI applications. We propose a novel learning-based approach to automatically recalibrating hyperspectral images during surgery.
  arXiv  Detail & Related papers  (2024-09-11T08:30:03Z)
• Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images [0.0]
  Chemical staining methods are dependable but require extensive time, expensive chemicals, and raise environmental concerns. Generative AI technologies are pivotal in addressing these issues. Our work introduces the use of generative AI for virtual staining, aiming to enhance performance, trustworthiness, scalability, and adaptability in computational pathology.
  arXiv  Detail & Related papers  (2024-06-26T21:52:05Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology [1.488519799639108]
  Machine learning algorithms have the potential to improve patient outcomes in digital pathology. generalization is limited by sensitivity to variations in tissue preparation, staining procedures and scanning equipment. We studied the effectiveness of two Synthetic DOmain-Targeted Augmentation (S-DOTA) methods, namely CycleGAN-enabled Scanner Transform (ST) and targeted Stain Vector Augmentation (SVA) We evaluated the ability of these techniques to improve model generalization to various tasks and settings.
  arXiv  Detail & Related papers  (2023-05-03T19:53:30Z)
• Hospital-Agnostic Image Representation Learning in Digital Pathology [0.7412445894287709]
  Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability in the histopathology images. A domain generalization technique is leveraged in this study to improve the generalization capability of a Deep Neural Network (DNN)
  arXiv  Detail & Related papers  (2022-04-05T11:45:46Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms [0.9137554315375919]
  In clinical diagnosis, medical images may contain fault artifacts, introduced due to noise, blur and faulty equipment. In this paper, automated image quality improvement approaches for adapted and benchmarked for the task of medical image super-resolution.
  arXiv  Detail & Related papers  (2022-01-17T07:27:03Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.