RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection Article Swipe
Soumen Basu
,
Mayank Gupta
,
Pratyaksha Rana
,
Pankaj Gupta
,
Chetan Arora
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.04793
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.04793
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined using a contemporary transformer architecture for highly accurate Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our experiments indicate that the detection accuracy of our model beats even human radiologists, and advocates its use as the second reader for GBC diagnosis. Bag of words embeddings allow our model to be probed for generating interpretable explanations for GBC detection consistent with the ones reported in medical literature. We show that the proposed model not only helps understand decisions of neural network models but also aids in discovery of new visual features relevant to the diagnosis of GBC. Source-code and model will be available at https://github.com/sbasu276/RadFormer
Related Topics
Concepts
Architecture
Computer science
Transformer
Artificial intelligence
Feature (linguistics)
Cancer detection
Artificial neural network
Pattern recognition (psychology)
Code (set theory)
Deep neural networks
Representation (politics)
Machine learning
Cancer
Medicine
Geography
Engineering
Voltage
Electrical engineering
Philosophy
Internal medicine
Law
Political science
Archaeology
Linguistics
Politics
Set (abstract data type)
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.04793
- https://arxiv.org/pdf/2211.04793
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308757867
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308757867Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.04793Digital Object Identifier
- Title
-
RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-09Full publication date if available
- Authors
-
Soumen Basu, Mayank Gupta, Pratyaksha Rana, Pankaj Gupta, Chetan AroraList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.04793Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.04793Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2211.04793Direct OA link when available
- Concepts
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Architecture, Computer science, Transformer, Artificial intelligence, Feature (linguistics), Cancer detection, Artificial neural network, Pattern recognition (psychology), Code (set theory), Deep neural networks, Representation (politics), Machine learning, Cancer, Medicine, Geography, Engineering, Voltage, Electrical engineering, Philosophy, Internal medicine, Law, Political science, Archaeology, Linguistics, Politics, Set (abstract data type), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 110, 131 |
| abstract_inverted_index.of | 24, 30, 70, 89, 124, 133, 141 |
| abstract_inverted_index.to | 8, 95, 138 |
| abstract_inverted_index.Bag | 88 |
| abstract_inverted_index.GBC | 86, 103 |
| abstract_inverted_index.Our | 16, 63 |
| abstract_inverted_index.The | 39 |
| abstract_inverted_index.and | 26, 41, 77, 144 |
| abstract_inverted_index.are | 45 |
| abstract_inverted_index.bag | 29 |
| abstract_inverted_index.but | 128 |
| abstract_inverted_index.for | 12, 22, 52, 85, 98, 102 |
| abstract_inverted_index.its | 79 |
| abstract_inverted_index.new | 134 |
| abstract_inverted_index.not | 119 |
| abstract_inverted_index.our | 71, 93 |
| abstract_inverted_index.the | 67, 82, 107, 116, 139 |
| abstract_inverted_index.use | 80 |
| abstract_inverted_index.GBC. | 142 |
| abstract_inverted_index.aids | 130 |
| abstract_inverted_index.also | 129 |
| abstract_inverted_index.deep | 4, 33 |
| abstract_inverted_index.even | 74 |
| abstract_inverted_index.from | 59 |
| abstract_inverted_index.maps | 44 |
| abstract_inverted_index.ones | 108 |
| abstract_inverted_index.only | 120 |
| abstract_inverted_index.show | 114 |
| abstract_inverted_index.that | 66, 115 |
| abstract_inverted_index.then | 27 |
| abstract_inverted_index.will | 146 |
| abstract_inverted_index.with | 36, 106 |
| abstract_inverted_index.(GBC) | 57 |
| abstract_inverted_index.(USG) | 61 |
| abstract_inverted_index.allow | 92 |
| abstract_inverted_index.beats | 73 |
| abstract_inverted_index.helps | 121 |
| abstract_inverted_index.human | 75 |
| abstract_inverted_index.image | 14 |
| abstract_inverted_index.learn | 9 |
| abstract_inverted_index.local | 37, 42 |
| abstract_inverted_index.model | 72, 94, 118, 145 |
| abstract_inverted_index.novel | 3 |
| abstract_inverted_index.style | 32 |
| abstract_inverted_index.using | 47 |
| abstract_inverted_index.words | 31, 90 |
| abstract_inverted_index.Cancer | 56 |
| abstract_inverted_index.global | 20 |
| abstract_inverted_index.highly | 53 |
| abstract_inverted_index.learns | 28 |
| abstract_inverted_index.models | 127 |
| abstract_inverted_index.neural | 5, 125 |
| abstract_inverted_index.probed | 97 |
| abstract_inverted_index.reader | 84 |
| abstract_inverted_index.region | 23 |
| abstract_inverted_index.second | 83 |
| abstract_inverted_index.visual | 135 |
| abstract_inverted_index.feature | 34, 43 |
| abstract_inverted_index.global, | 40 |
| abstract_inverted_index.images. | 62 |
| abstract_inverted_index.medical | 13, 111 |
| abstract_inverted_index.network | 6, 126 |
| abstract_inverted_index.propose | 1 |
| abstract_inverted_index.accuracy | 69 |
| abstract_inverted_index.accurate | 54 |
| abstract_inverted_index.combined | 46 |
| abstract_inverted_index.features | 136 |
| abstract_inverted_index.indicate | 65 |
| abstract_inverted_index.proposed | 117 |
| abstract_inverted_index.relevant | 137 |
| abstract_inverted_index.reported | 109 |
| abstract_inverted_index.advocates | 78 |
| abstract_inverted_index.analysis. | 15 |
| abstract_inverted_index.attention | 21 |
| abstract_inverted_index.available | 148 |
| abstract_inverted_index.decisions | 123 |
| abstract_inverted_index.detection | 58, 68, 104 |
| abstract_inverted_index.diagnosis | 140 |
| abstract_inverted_index.discovery | 132 |
| abstract_inverted_index.generates | 18 |
| abstract_inverted_index.interest, | 25 |
| abstract_inverted_index.Ultrasound | 60 |
| abstract_inverted_index.attention. | 38 |
| abstract_inverted_index.consistent | 105 |
| abstract_inverted_index.diagnosis. | 87 |
| abstract_inverted_index.embeddings | 35, 91 |
| abstract_inverted_index.generating | 99 |
| abstract_inverted_index.understand | 122 |
| abstract_inverted_index.Gallbladder | 55 |
| abstract_inverted_index.Source-code | 143 |
| abstract_inverted_index.experiments | 64 |
| abstract_inverted_index.literature. | 112 |
| abstract_inverted_index.transformer | 50 |
| abstract_inverted_index.architecture | 7, 17, 51 |
| abstract_inverted_index.contemporary | 49 |
| abstract_inverted_index.explanations | 101 |
| abstract_inverted_index.interpretable | 10, 100 |
| abstract_inverted_index.radiologists, | 76 |
| abstract_inverted_index.representation | 11 |
| abstract_inverted_index.https://github.com/sbasu276/RadFormer | 150 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |