Tumor detection under cystoscopy with transformer-augmented deep learning algorithm Article Swipe
Xiao Jia
,
Eugene Shkolyar
,
Mark Laurie
,
Okyaz Eminağa
,
Joseph C. Liao
,
Lei Xing
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ace499
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ace499
Objective. Accurate tumor detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. Advanced deep learning algorithms hold the potential to improve the performance of standard white-light cystoscopy (WLC) in a noninvasive and cost-effective fashion. The purpose of this work is to develop a cost-effective, transformer-augmented deep learning algorithm for accurate detection of bladder tumors in WLC and to assess its performance on archived patient data. Approach. ‘CystoNet-T’, a deep learning-based bladder tumor detector, was developed with a transformer-augmented pyramidal CNN architecture to improve automated tumor detection of WLC. CystoNet-T incorporated the self-attention mechanism by attaching transformer encoder modules to the pyramidal layers of the feature pyramid network (FPN), and obtained multi-scale activation maps with global features aggregation. Features resulting from context augmentation served as the input to a region-based detector to produce tumor detection predictions. The training set was constructed by 510 WLC frames that were obtained from cystoscopy video sequences acquired from 54 patients. The test set was constructed based on 101 images obtained from WLC sequences of 13 patients. Main results. CystoNet-T was evaluated on the test set with 96.4 F1 and 91.4 AP (Average Precision). This result improved the benchmark of Faster R-CNN and YOLO by 7.3 points in F1 and 3.8 points in AP. The improvement is attributed to the strong ability of global attention of CystoNet-T and better feature learning of the pyramids architecture throughout the training. The model was found to be particularly effective in highlighting the foreground information for precise localization of the true positives while favorably avoiding false alarms Significance. We have developed a deep learning algorithm that accurately detects bladder tumors in WLC. Transformer-augmented AI framework promises to aid in clinical decision-making for improved bladder cancer diagnosis and therapeutic guidance.
Related Topics
Concepts
Computer science
Cystoscopy
Deep learning
Artificial intelligence
Transformer
Test set
Bladder cancer
Algorithm
Encoder
Pyramid (geometry)
Pattern recognition (psychology)
Cancer
Medicine
Mathematics
Pathology
Geometry
Internal medicine
Physics
Voltage
Alternative medicine
Quantum mechanics
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/ace499
- OA Status
- green
- Cited By
- 15
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385620438
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385620438Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/ace499Digital Object Identifier
- Title
-
Tumor detection under cystoscopy with transformer-augmented deep learning algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-07Full publication date if available
- Authors
-
Xiao Jia, Eugene Shkolyar, Mark Laurie, Okyaz Eminağa, Joseph C. Liao, Lei XingList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/ace499Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10697018/pdf/nihms-1945504.pdfDirect OA link when available
- Concepts
-
Computer science, Cystoscopy, Deep learning, Artificial intelligence, Transformer, Test set, Bladder cancer, Algorithm, Encoder, Pyramid (geometry), Pattern recognition (psychology), Cancer, Medicine, Mathematics, Pathology, Geometry, Internal medicine, Physics, Voltage, Alternative medicine, Quantum mechanics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 7Per-year citation counts (last 5 years)
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.region-based | 133 |
| abstract_inverted_index.Significance. | 262 |
| abstract_inverted_index.cost-effective | 37 |
| abstract_inverted_index.learning-based | 74 |
| abstract_inverted_index.self-attention | 96 |
| abstract_inverted_index.cost-effective, | 48 |
| abstract_inverted_index.decision-making | 285 |
| abstract_inverted_index.‘CystoNet-T’, | 71 |
| abstract_inverted_index.Transformer-augmented | 277 |
| abstract_inverted_index.transformer-augmented | 49, 82 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.96430067 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |