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Gigapixel end-to-en...
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Dooper, StephanRadboud Univ Nijmegen, Netherlands
(author)
Gigapixel end-to-end training using streaming and attention
- Article/chapterEnglish2023
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ELSEVIER,2023
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electronicrdacarrier
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LIBRIS-ID:oai:DiVA.org:liu-196681
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-196681URI
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https://doi.org/10.1016/j.media.2023.102881DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
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Funding Agencies|Innovative Medicines Initiative 2 Joint Undertaking [945358]; European Union; EFPIA, Belgium
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Current hardware limitations make it impossible to train convolutional neural networks on gigapixel image inputs directly. Recent developments in weakly supervised learning, such as attention-gated multiple instance learning, have shown promising results, but often use multi-stage or patch-wise training strategies risking suboptimal feature extraction, which can negatively impact performance. In this paper, we propose to train a ResNet-34 encoder with an attention-gated classification head in an end-to-end fashion, which we call StreamingCLAM, using a streaming implementation of convolutional layers. This allows us to train end-to-end on 4-gigapixel microscopic images using only slide-level labels.We achieve a mean area under the receiver operating characteristic curve of 0.9757 for metastatic breast cancer detection (CAMELYON16), close to fully supervised approaches using pixel-level annotations. Our model can also detect MYC-gene translocation in histologic slides of diffuse large B-cell lymphoma, achieving a mean area under the ROC curve of 0.8259. Furthermore, we show that our model offers a degree of interpretability through the attention mechanism.
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Pinckaers, HansRadboud Univ Nijmegen, Netherlands
(author)
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Aswolinskiy, WitaliRadboud Univ Nijmegen, Netherlands
(author)
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Hebeda, KonnieRadboud Univ Nijmegen, Netherlands
(author)
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Jarkman, SofiaLinköpings universitet,Avdelningen för neurobiologi,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Klinisk patologi(Swepub:liu)sofja84
(author)
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van der Laak, JeroenLinköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands(Swepub:liu)jerva26
(author)
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Litjens, GeertRadboud Univ Nijmegen, Netherlands
(author)
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Radboud Univ Nijmegen, NetherlandsAvdelningen för neurobiologi
(creator_code:org_t)
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In:Medical Image Analysis: ELSEVIER881361-84151361-8423
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