A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology

• URL: http://arxiv.org/abs/2409.04615v1
• Date: Fri, 6 Sep 2024 20:56:25 GMT
• Title: A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology
• Authors: S. Hemati, Krishna R. Kalari, H. R. Tizhoosh,
• Abstract summary: Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs) WSIs are gigapixel files that capture the intricate details of tissue samples, providing a rich source of information for diagnostic and research purposes. Due to their enormous size, representing these images as one compact vector is essential for many computational pathology tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel files that capture the intricate details of tissue samples, providing a rich source of information for diagnostic and research purposes. However, due to their enormous size, representing these images as one compact vector is essential for many computational pathology tasks, such as search and retrieval, to ensure efficiency and scalability. Most current methods are "patch-oriented," meaning they divide WSIs into smaller patches for processing, which prevents a holistic analysis of the entire slide. Additionally, the necessity for compact representation is driven by the expensive high-performance storage required for WSIs. Not all hospitals have access to such extensive storage solutions, leading to potential disparities in healthcare quality and accessibility. This paper provides an overview of existing set-based approaches to single-vector WSI representation, highlighting the innovations that allow for more efficient and effective use of these complex images in digital pathology, thus addressing both computational challenges and storage limitations.

Related papers

• Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images [2.6733991338938026]
  Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs.
  arXiv  Detail & Related papers  (2024-11-13T11:25:05Z)
• Efficient Whole Slide Image Classification through Fisher Vector Representation [2.4472081831862655]
  This study introduces a novel method for WSI classification by automating the identification and examination of the most informative patches. Our method involves two-stages: firstly, it extracts only a few patches from the WSIs based on their pathological significance; and secondly, it employs Fisher vectors for representing features extracted from these patches. This approach not only accentuates key pathological features within the WSI representation but also significantly reduces computational overhead, thus making the process more efficient and scalable.
  arXiv  Detail & Related papers  (2024-11-13T11:24:12Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• SPLICE -- Streamlining Digital Pathology Image Processing [0.7852714805965528]
  We propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE) SPLICE condenses a histopathology WSI into a compact set of representative patches, forming a "collage" of WSI while minimizing redundancy. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%.
  arXiv  Detail & Related papers  (2024-04-26T21:30:36Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• A self-supervised framework for learning whole slide representations [52.774822784847565]
  We present Slide Pre-trained Transformers (SPT) for gigapixel-scale self-supervision of whole slide images. We benchmark SPT visual representations on five diagnostic tasks across three biomedical microscopy datasets.
  arXiv  Detail & Related papers  (2024-02-09T05:05:28Z)
• Analysis and Validation of Image Search Engines in Histopathology [1.2431249807060922]
  Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. matching WSI to WSI can serve as the critical method for patient matching. We report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants.
  arXiv  Detail & Related papers  (2024-01-06T18:17:55Z)
• Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images [0.6037276428689636]
  We introduce a new evolutionary approach for WSI representation based on large-scale multi-objective optimization (LSMOP) of deep embeddings. We validate the proposed schemes using The Cancer Genome Atlas (TC) images in terms of WSI representation, classification accuracy, and feature quality. The proposed evolutionary algorithm finds a very compact feature vector to represent a WSI with 8% higher accuracy compared to the codes provided by the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-02T03:36:15Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)
• Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective [69.99087941471882]
  We study the problem of supporting multiple machine vision analytics tasks with the compressed visual representation. By utilizing the intrinsic transferability among different tasks, our framework successfully constructs compact and expressive representations at low bit-rates. In order to impose compactness in the representations, we propose a codebook-based hyperprior.
  arXiv  Detail & Related papers  (2021-06-16T01:44:32Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.