bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/408237

Deep learning based retrieval system for gigapixel histopathology cases and open access literature

Otalora, S.·Schaer, R.·Atzori, M.·Jimenez del Toro, O. A.·Muller, H.
View original source ↗Export metadata ↓

Abstract

Clinical practice is getting increasingly stressful for pathologists due to increasing complexity and time constraints. Histopathology is slowly shifting to digital pathology, thus creating opportunities to allow pathologists to improve reading quality or save time using Artificial Intelligence (AI)-based applications. We aim to enhance the practice of pathologists through a retrieval system that allows them to simplify their workflow, limit the need for second opinions, while also learning in the process. In this work, an innovative retrieval system for digital pathology is integrated within a Whole Slide Image (WSI) viewer, allowing to define regions of interest in images as queries for finding visually similar areas using deep representations. The back-end similarity computation algorithms are based on a multimodal approach, allowing to exploit both text information and content-based image features. Shallow and deep representations of the images were evaluated, the later showed a better overall retrieval performance in a set of 112 whole slide images from biopsies. The system was also tested by pathologists, highlighting its capabilities and suggesting possible ways to improve it and make it more usable in clinical practice. The retrieval system developed can enhance the practice of pathologists by enabling them to use their experience and knowledge to properly control artificial intelligence tools for navigating repositories of images for decision support purposes.

Explore related subjects

pathologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Otalora, S., Schaer, R., Atzori, M., Jimenez del Toro, O. A., Muller, H.. 2018-09-06. Deep learning based retrieval system for gigapixel histopathology cases and open access literature. https://doi.org/10.1101/408237

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Digital pathological classification of peripheral neuroblastic tumors across multi-continent cohorts

Peripheral neuroblastic tumors (pNTs) are embryonal tumors of neural crest origin with highly variable clinical behaviors, ranging from spontaneous regression to aggressive and fatal disease. The International Neuroblastoma Pathology Classification (INPC) defines four diagnostic categories and stratifies tumors into Favorable Histology (FH) or Unfavorable Histology (UH) group based on age-linked assessment of tumor differentiation/maturation and the Mitosis-Karyorrhexis Index (MKI). Prognostic distinction according to INPC offers significant prediction of clinical outcomes, with patients harboring FH tumors demonstrating longer overall and event-free survival than those with UH tumor. Here, we assembled the largest multi-continental whole-slide histology imaging dataset of pNTs to date, comprising 2,030 tumors from the United States, Canada, Australia, New Zealand, Germany and Japan. Using this dataset, we developed NB-PathAssist, a computational pathology framework for automated INPC-based classification from digitized whole-slide images. NB-PathAssist integrates foundation-model-based feature extraction with an archetype-guided SummaryMixing architecture that compresses whole-slide morphology into representative histologic tokens for tumor-level classification. To automate MKI assessment, we manually annotated 14,049 nuclei, classifying them as mitotic-karyorrhectic (MK) tumor cells, non-MK tumor cells, or non-tumor cells. The resulting model was applied to phenotype cells in whole-slide images across 1,649 tissue sections. Across geographically and institutionally diverse cohorts, NB-PathAssist reproducibly assessed key diagnostic features and generated prognostic classifications concordant with reference INPC annotations. The average balanced accuracy for classifying tumor categories, grade of neuroblastic differentiation, MKI and prognostic groups achieved 0.84, 0.86, 0.89 and 0.96, respectively. We packaged the NB-PathAssist algorithms into an end-to-end online platform for scalable and standardized AI-assisted assessment of pNTs.

pathology↗
bioRxiv2026

Epidemic Risks associated with high-temperature replicating infectious hematopoietic necrosis virus (IHNV) field isolates: from molecular characterization to host pathogen interactions

Infectious hematopoietic necrosis virus (IHNV) is a WOAH-notifiable pathogen responsible for severe acute disease in salmonids at temperatures ranging from 4 to 14{degrees}. At higher water temperatures, the disease is generally associated with reduced mortality and lower viral replication. In the context of climate change, rising water temperatures could favor the emergence of thermotolerant strains while representing a major physiological and metabolic stress potentially altering fish's immune responses. This study aims to characterize the impact of increasing water temperatures on IHNV replication and host-pathogen interactions. Following pre-screening, the replication kinetics of the reference strain N61 and two field isolates RtNag0a and SA-22 were characterized in EPC (Epithelioma Papulosum Cyprini) and RTG-2 (Rainbow trout gonad) cell lines at 14, 20 and 22{degrees}. Virulence and host-pathogen interactions were subsequently investigated in vivo in zebrafish (Zf) larvae using complementary microinjection and bath immersion infection models at 21.5 and 23.5{degrees}. In vitro studies revealed distinct thermal replication profiles, distinguishing thermosensitive from thermotolerant isolates, although these phenotypes were influenced by the cellular environment. The zebrafish model enabled the characterization of virulence and antiviral host responses associated with thermotolerant IHNV isolates. Notably, waterborne infection proved effective, contrasting with previous reports describing this exposure route as unsuitable for IHNV studies. These findings expand the growing list of emerging European IHNV genotypes capable of replicating at elevated temperatures (>20{degrees}) and establish zebrafish larvae as a relevant model for virulence assessment of field isolates. Together, these results provide valuable insights into the potential impact of climate warming on IHNV epidemiology and support efforts to predict and anticipate future outbreaks under changing environmental conditions.

pathology↗
bioRxiv2026

Arthroscopic Raman Probe Provides Biomarkers of TMJ Tissue Composition that Account for Tissue Function

Background: Temporomandibular joint (TMJ) disorders affect up to 25% of the population and may require diverse surgical interventions. However, intraoperative decision-making relies largely on visual assessment, which does not quantitatively characterize tissue composition or mechanical function. Raman spectroscopy offers a nondestructive approach to molecular-level tissue characterization, but its application to TMJ tissues remains unvalidated. Methods: An arthroscopy-compatible Raman probe was used to characterize mandibular condyles (n=15 porcine, n=11 human) and human articular discs (n=7). Raman-derived biomarkers associated with glycosaminoglycans (GAG), collagen (COL), water (H2O), and hydroxyapatite (HA) were evaluated against condylar indentation modulus and disc tensile modulus. Arthroscopic measurements in cadaveric TMJs assessed technical feasibility, while hyperspectral Raman imaging characterized spatial compositional distributions. Results: For condylar cartilage, combined GAG and H2O biomarkers accounted for 86% of the variation in indentation modulus (R2=0.86), compared with 4% for Outerbridge grading (R2=0.04). For articular discs, combined proline and water-associated biomarkers accounted for 88% of the variation in tensile modulus (R2=0.88). Raman spectra acquired in situ under simulated arthroscopic conditions were comparable in quality to ex vivo measurements. Raman imaging revealed distinct compositional patterns in healthy and degenerated tissues, including GAG depletion and cartilage thinning in degenerated condylar cartilage. Conclusions: Raman-derived biomarkers are strongly associated with TMJ cartilage and disc mechanical properties and outperform Outerbridge grading in explaining variation in condylar indentation modulus. Arthroscopic Raman spectroscopy is technically feasible and may complement visual assessment by providing objective compositional information to support future development of tissue-informed surgical decision-making.

pathology↗