Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours
| Autor: | Carlos Hernandez Perez, Marc Combalia Escudero, Susana Puig, Josep Malvehy, Veronica Vilaplana Besler |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC]
Attention-based multiple instance learning Deep learning Early detection Whole slide image Contrastive learning Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo [Àrees temàtiques de la UPC] Intel·ligència artificial -- Aplicacions a la medicina Melanoma Artificial intelligence -- Medical applications Aprenentatge profund |
| Zdroj: | Cancer Prevention Through Early Detection ISBN: 9783031179785 |
| Popis: | Sentinel lymph node status is a crucial prognosis factor for melanomas; nonetheless, the invasive surgery required to obtain it always puts the patient at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited datasets. We propose a pre-training strategy based on self-supervised contrastive learning to extract better image feature representations and an attention-based Multiple Instance Learning approach to enhance the model’s performance. With this work, we quantitatively demonstrate that combining both methods improves various classification metrics and qualitatively show that contrastive learning encourages the network to output higher attention scores to tumour tissue and lower scores to image artifacts. Work supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/AEI/10.13039/501100011033 and the project 718/C/ 2019 funded by Fundació la Marato de TV3. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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