Histopathology Slide Indexing and Search: Are We There Yet?
- URL: http://arxiv.org/abs/2306.17019v2
- Date: Thu, 4 Jan 2024 22:54:18 GMT
- Title: Histopathology Slide Indexing and Search: Are We There Yet?
- Authors: Helen H. Shang, Mohammad Sadegh Nasr, Jai Prakash Veerla, Parisa
Boodaghi Malidarreh, MD Jillur Rahman Saurav, Amir Hajighasemi, Manfred
Huber, Chace Moleta, Jitin Makker, Jacob M. Luber
- Abstract summary: We investigate the clinical readiness of three state-of-the-art histopathology slide search engines, Yottixel, SISH, and RetCCL, on three patients with solid tumors.
We found that all three image search engines fail to produce consistently reliable results and have difficulties in capturing granular and subtle features of malignancy.
- Score: 0.9867627975175174
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The search and retrieval of digital histopathology slides is an important
task that has yet to be solved. In this case study, we investigate the clinical
readiness of three state-of-the-art histopathology slide search engines,
Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a
qualitative assessment of each model's performance in providing retrieval
results that are reliable and useful to pathologists. We found that all three
image search engines fail to produce consistently reliable results and have
difficulties in capturing granular and subtle features of malignancy, limiting
their diagnostic accuracy. Based on our findings, we also propose a minimal set
of requirements to further advance the development of accurate and reliable
histopathology image search engines for successful clinical adoption.
Related papers
- HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis [9.615399811006034]
HistoGym aims to foster whole slide image diagnosis by mimicking the real-life processes of doctors.
We offer various scenarios for different organs and cancers, including both WSI-based and selected region-based scenarios.
arXiv Detail & Related papers (2024-08-16T17:19:07Z) - On Validation of Search & Retrieval of Tissue Images in Digital Pathology [0.0]
Medical images play a crucial role in modern healthcare by providing vital information for diagnosis, treatment planning, and disease monitoring.
The technological advancements have exponentially increased the volume and complexity of medical images.
Content-Based Image Retrieval (CBIR) systems address this need by searching and retrieving images based on visual content.
arXiv Detail & Related papers (2024-08-02T20:55:45Z) - Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens.
We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
arXiv Detail & Related papers (2024-04-15T02:41:55Z) - On Image Search in Histopathology [0.0]
We review the latest developments in image search technologies for histopathology.
We offer a concise overview tailored for computational pathology researchers seeking effective, fast and efficient image search methods in their work.
arXiv Detail & Related papers (2024-01-14T12:38:49Z) - 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) - Geometric Deep Learning to Identify the Critical 3D Structural Features
of the Optic Nerve Head for Glaucoma Diagnosis [52.06403518904579]
The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma.
We used PointNet and dynamic graph convolutional neural network (DGCNN) to diagnose glaucoma from 3D ONH point clouds.
Our approach may have strong potential to be used in clinical applications for the diagnosis and prognosis of a wide range of ophthalmic disorders.
arXiv Detail & Related papers (2022-04-14T12:52:10Z) - MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining
Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS)
MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020.
The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
arXiv Detail & Related papers (2022-01-10T06:37:23Z) - SpineOne: A One-Stage Detection Framework for Degenerative Discs and
Vertebrae [54.751251046196494]
We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices.
SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
arXiv Detail & Related papers (2021-10-28T12:59:06Z) - Efficient Multi-objective Evolutionary 3D Neural Architecture Search for
COVID-19 Detection with Chest CT Scans [25.03394794744372]
This paper proposes an efficient Multi-objective neural ARchitecture Search framework, which can automatically search for 3D neural architectures.
Within the framework, we use weight sharing strategy to significantly improve the search efficiency and finish the search process in 8 hours.
With the objectives of accuracy, potential, and model size, we find a lightweight model (3.39 MB), which outperforms three baseline human-designed models.
arXiv Detail & Related papers (2021-01-26T09:52:42Z) - Automated Model Design and Benchmarking of 3D Deep Learning Models for
COVID-19 Detection with Chest CT Scans [72.04652116817238]
We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification.
We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
arXiv Detail & Related papers (2021-01-14T03:45:01Z) - Spatio-spectral deep learning methods for in-vivo hyperspectral
laryngeal cancer detection [49.32653090178743]
Early detection of head and neck tumors is crucial for patient survival.
Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors.
We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
arXiv Detail & Related papers (2020-04-21T17:07:18Z)
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.