Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs Article Swipe
Neel Kanwal
,
Farbod Khoraminia
,
Umay Kiraz
,
Andrés Mosquera‐Zamudio
,
Carlos Monteagudo
,
Emiel A. M. Janssen
,
Tahlita C.M. Zuiverloon
,
Chunming Rong
,
Kjersti Engan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12911-024-02676-z
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12911-024-02676-z
Background Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis. Methods In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application. Results We extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme. Conclusions The proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo , respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12911-024-02676-z
- OA Status
- gold
- Cited By
- 8
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403176765
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403176765Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s12911-024-02676-zDigital Object Identifier
- Title
-
Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-07Full publication date if available
- Authors
-
Neel Kanwal, Farbod Khoraminia, Umay Kiraz, Andrés Mosquera‐Zamudio, Carlos Monteagudo, Emiel A. M. Janssen, Tahlita C.M. Zuiverloon, Chunming Rong, Kjersti EnganList of authors in order
- Landing page
-
https://doi.org/10.1186/s12911-024-02676-zPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1186/s12911-024-02676-zDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Convolutional neural network, Robustness (evolution), Digital pathology, Artifact (error), Pattern recognition (psychology), Deep learning, Machine learning, Image processing, Computer vision, Image (mathematics), Biochemistry, Chemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
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2025: 7, 2024: 1Per-year citation counts (last 5 years)
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82Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.images | 53 |
| abstract_inverted_index.models | 125, 176, 230 |
| abstract_inverted_index.neural | 181 |
| abstract_inverted_index.online | 430 |
| abstract_inverted_index.paper, | 91 |
| abstract_inverted_index.result | 32, 62 |
| abstract_inverted_index.scheme | 99 |
| abstract_inverted_index.scores | 261 |
| abstract_inverted_index.system | 81 |
| abstract_inverted_index.tested | 233 |
| abstract_inverted_index.tissue | 13, 29 |
| abstract_inverted_index.types, | 241 |
| abstract_inverted_index.unseen | 263 |
| abstract_inverted_index.vision | 185 |
| abstract_inverted_index.yields | 254 |
| abstract_inverted_index.(CPATH) | 80 |
| abstract_inverted_index.(DCNNs) | 183 |
| abstract_inverted_index.(ViTs). | 187 |
| abstract_inverted_index.(WSIs). | 54 |
| abstract_inverted_index.Methods | 88 |
| abstract_inverted_index.Results | 211 |
| abstract_inverted_index.between | 404 |
| abstract_inverted_index.capture | 129 |
| abstract_inverted_index.damaged | 106 |
| abstract_inverted_index.dataset | 426 |
| abstract_inverted_index.equally | 414 |
| abstract_inverted_index.experts | 97, 127, 320 |
| abstract_inverted_index.further | 312 |
| abstract_inverted_index.highest | 340 |
| abstract_inverted_index.improve | 152 |
| abstract_inverted_index.include | 170 |
| abstract_inverted_index.mixture | 95 |
| abstract_inverted_index.models. | 220, 288 |
| abstract_inverted_index.notable | 103 |
| abstract_inverted_index.overall | 354 |
| abstract_inverted_index.prepare | 19 |
| abstract_inverted_index.propose | 93 |
| abstract_inverted_index.provide | 378 |
| abstract_inverted_index.quality | 305, 379 |
| abstract_inverted_index.report, | 306 |
| abstract_inverted_index.scheme. | 361 |
| abstract_inverted_index.schemes | 226 |
| abstract_inverted_index.simpler | 228 |
| abstract_inverted_index.slides, | 48 |
| abstract_inverted_index.tissue, | 107, 110 |
| abstract_inverted_index.version | 45 |
| abstract_inverted_index.yielded | 247 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.artifact | 131, 208, 296, 332, 365, 388 |
| abstract_inverted_index.assessed | 321 |
| abstract_inverted_index.bubbles, | 112 |
| abstract_inverted_index.control. | 380 |
| abstract_inverted_index.datasets | 235 |
| abstract_inverted_index.detailed | 395 |
| abstract_inverted_index.ensemble | 135 |
| abstract_inverted_index.evaluate | 164 |
| abstract_inverted_index.external | 197 |
| abstract_inverted_index.involves | 11 |
| abstract_inverted_index.learning | 68 |
| abstract_inverted_index.networks | 182 |
| abstract_inverted_index.pipeline | 367, 386 |
| abstract_inverted_index.proposed | 216, 252, 364 |
| abstract_inverted_index.reliable | 85, 372 |
| abstract_inverted_index.results. | 250 |
| abstract_inverted_index.solution | 413 |
| abstract_inverted_index.standard | 6 |
| abstract_inverted_index.Artifacts | 55 |
| abstract_inverted_index.agreement | 341, 351 |
| abstract_inverted_index.analysis. | 314 |
| abstract_inverted_index.artifacts | 75 |
| abstract_inverted_index.automated | 86 |
| abstract_inverted_index.detecting | 72, 101 |
| abstract_inverted_index.detection | 209, 333, 366, 389 |
| abstract_inverted_index.developed | 159 |
| abstract_inverted_index.different | 237 |
| abstract_inverted_index.digitized | 44 |
| abstract_inverted_index.essential | 83 |
| abstract_inverted_index.evaluated | 195, 214 |
| abstract_inverted_index.excluding | 74 |
| abstract_inverted_index.hospitals | 238 |
| abstract_inverted_index.including | 105 |
| abstract_inverted_index.inference | 270 |
| abstract_inverted_index.pathology | 79 |
| abstract_inverted_index.pipelines | 162, 190 |
| abstract_inverted_index.potential | 300 |
| abstract_inverted_index.retaining | 265 |
| abstract_inverted_index.specimens | 14 |
| abstract_inverted_index.trade-off | 403 |
| abstract_inverted_index.usability | 356 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.DCNN-based | 359 |
| abstract_inverted_index.Therefore, | 71 |
| abstract_inverted_index.ViTs-based | 224 |
| abstract_inverted_index.artifacts, | 37, 104 |
| abstract_inverted_index.diagnosis. | 9, 87 |
| abstract_inverted_index.diagnostic | 355 |
| abstract_inverted_index.extracting | 12 |
| abstract_inverted_index.indicating | 349 |
| abstract_inverted_index.irrelevant | 58, 115 |
| abstract_inverted_index.mechanism. | 141 |
| abstract_inverted_index.multiclass | 175, 219, 229, 287 |
| abstract_inverted_index.particular | 130 |
| abstract_inverted_index.predictive | 323 |
| abstract_inverted_index.procedures | 31 |
| abstract_inverted_index.processing | 30 |
| abstract_inverted_index.relatively | 282 |
| abstract_inverted_index.robustness | 206 |
| abstract_inverted_index.suspicious | 16 |
| abstract_inverted_index.trade-offs | 285 |
| abstract_inverted_index.translated | 342 |
| abstract_inverted_index.ultimately | 40 |
| abstract_inverted_index.(MobileNet) | 245 |
| abstract_inverted_index.Conclusions | 362 |
| abstract_inverted_index.DCNNs-based | 221 |
| abstract_inverted_index.algorithms. | 70 |
| abstract_inverted_index.complexity, | 408 |
| abstract_inverted_index.evaluation, | 318 |
| abstract_inverted_index.experiments | 396 |
| abstract_inverted_index.extensively | 213 |
| abstract_inverted_index.independent | 122 |
| abstract_inverted_index.microscopic | 25 |
| abstract_inverted_index.morphology. | 132 |
| abstract_inverted_index.performance | 167, 277, 324, 405 |
| abstract_inverted_index.predictions | 65, 137, 374 |
| abstract_inverted_index.probability | 149 |
| abstract_inverted_index.qualitative | 317 |
| abstract_inverted_index.sensitivity | 154, 260 |
| abstract_inverted_index.substantial | 350 |
| abstract_inverted_index.trade-offs. | 168 |
| abstract_inverted_index.transferred | 41 |
| abstract_inverted_index.Furthermore, | 289 |
| abstract_inverted_index.application. | 210 |
| abstract_inverted_index.distribution | 150 |
| abstract_inverted_index.examination. | 26 |
| abstract_inverted_index.histological | 28 |
| abstract_inverted_index.introduction | 35 |
| abstract_inverted_index.outperformed | 227 |
| abstract_inverted_index.segmentation | 297 |
| abstract_inverted_index.thresholding | 145 |
| abstract_inverted_index.transformers | 186 |
| abstract_inverted_index.applications. | 421 |
| abstract_inverted_index.artifact-free | 301, 335 |
| abstract_inverted_index.computational | 78, 165, 267, 284, 313, 407 |
| abstract_inverted_index.convolutional | 180 |
| abstract_inverted_index.preservation, | 337 |
| abstract_inverted_index.probabilistic | 144 |
| abstract_inverted_index.qualitatively | 194 |
| abstract_inverted_index.respectively. | 436 |
| abstract_inverted_index.HistoArtifacts | 425 |
| abstract_inverted_index.Histopathology | 2 |
| abstract_inverted_index.diagnostically | 57 |
| abstract_inverted_index.histologically | 114 |
| abstract_inverted_index.quantitatively | 192 |
| abstract_inverted_index.best-performing | 385 |
| abstract_inverted_index.post-processing | 292 |
| abstract_inverted_index.straightforward | 411 |
| abstract_inverted_index.artifact-refined | 309 |
| abstract_inverted_index.generalizability | 204 |
| abstract_inverted_index.state-of-the-art | 178 |
| abstract_inverted_index.out-of-distribution | 199 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5074795857 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I92008406 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.94192032 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |