Academic Journal
Bridging Domains in Melanoma Diagnostics: Predicting BRAF Mutations and Sentinel Lymph Node Positivity with Attention-Based Models in Histological Images
| Τίτλος: | Bridging Domains in Melanoma Diagnostics: Predicting BRAF Mutations and Sentinel Lymph Node Positivity with Attention-Based Models in Histological Images |
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| Συγγραφείς: | Hernández Pérez, Carlos, Jiménez Martín, Lauren, Vilaplana Besler, Verónica |
| Πηγή: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Στοιχεία εκδότη: | IEEE, 2024. |
| Έτος έκδοσης: | 2024 |
| Θεματικοί όροι: | Predictive models, Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica, Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic, Pathology, Biological system modeling, Adaptation models, Analytical models, Melanoma, Supervised learning |
| Περιγραφή: | Whole Slide Images (WSIs) have significantly advanced the field of pathology by providing highly detailed views of tissue samples. Integrating Deep Learning (DL) into this area of research, particularly through transformer-based foundational models, has marked a new era in automated image analysis. These foundational models are adept at extracting features from WSIs, an essential step in their analysis process. The subsequent application of weakly supervised learning techniques combines these features to predict critical biomarkers, such as BRAF mutations and sentinel lymph node (SLN) biopsy positivity, which are vital in guiding patient treatment strategies. However, the limited availability of labelled datasets in pathology hinders the usefulness of DL models. Domain adaptation strategies adeptly overcome this hurdle, enabling model knowledge transfer between different tissue types, thus addressing data scarcity. Our study employs a form of domain adaptation by fine-tuning two DINOv2 models, one pre-trained on natural images and the other on WSI of colorectal cancer from the TCGA dataset, adapting them for melanoma analysis. We also incorporate a comparison with features extracted by a third DINOv1 model trained solely on WSIs of breast cancer. With this approach, we find some notable success in detecting BRAF mutations. Nonetheless, predicting SLN positivity presents a more intricate challenge, largely due to the indirect correlation between local histopathological features in WSIs of primary tumours and lymph node metastasis manifestation. This dual-faceted approach not only combats the issue of limited data but also showcases the potential for enhanced accuracy in the field of digital pathology. This research was 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 with id 201923-30 and 201923-31 funded by Fundació la Marato de TV3. Also by the FI-AGAUR ( 2022 FI B 00634 ) grant funded by Direcció General de Recerca (DGR) of Departament de Recerca i Universitats (REU) of the Generalitat de Catalunya and the European Social Fund. |
| Τύπος εγγράφου: | Article Conference object |
| Περιγραφή αρχείου: | application/pdf |
| DOI: | 10.1109/cvprw63382.2024.00520 |
| Σύνδεσμος πρόσβασης: | https://hdl.handle.net/2117/419471 https://doi.org/10.1109/cvprw63382.2024.00520 |
| Rights: | STM Policy #29 |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....dc49fe6d8f5c7d085722e0e02935e5dc |
| Βάση Δεδομένων: | OpenAIRE |
| DOI: | 10.1109/cvprw63382.2024.00520 |
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