ArticleLaboratory investigation; a journal of technical methods and pathology2021
GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.
Article in Laboratory investigation; a journal of technical methods and pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 38 citations in OpenAlex.
- Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- Article
- Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise.Archives of pathology & laboratory medicine · 2025Article
- Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective.Journal of imaging · 2025Review
- Review
- Benchmarking pathology foundation models for non-neoplastic pathology in the placenta.medRxiv : the preprint server for health sciences · 2025Article
- Cross-modal contrastive learning for unified placenta analysis using photographs.Patterns (New York, N.Y.) · 2024Article
- Quantitative Modeling to Characterize Maternal Inflammatory Response of Histologic Chorioamnionitis in Placental Membranes.American journal of reproductive immunology (New York, N.Y. : 1989) · 2024Article
- Identifying First-Trimester Risk Factors for SGA-LGA Using Weighted Inheritance Voting Ensemble Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.Nature communications · 2024Article
- Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- Article
- Enhancing Automatic Placenta Analysis through Distributional Feature Recomposition in Vision-Language Contrastive Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning.NPJ precision oncology · 2023Article
- A Patch-Based Deep Learning Approach for Detecting Rib Fractures on Frontal Radiographs in Young Children.Journal of digital imaging · 2023Article
- Tissue contamination challenges the credibility of machine learning models in real world digital pathology.medRxiv : the preprint server for health sciences · 2023Article
- Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus.Placenta · 2023Article
- Training of Deep Learning Pipelines on Memory-Constrained GPUs via Segmented Fused-Tiled Execution.Compiler construction : ... International Conference, CC ... : proceedings. CC (Conference) · 2022Article
- Project SWAVE 2.0: An overview of the study design for multimodal placental image acquisition and alignment.MethodsX · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and infant. Placental villi undergo a complex but reproducible sequence of maturation across the third-trimester. Abnormalities of villous maturation are a feature of gestational diabetes and preeclampsia, among others, but there is significant interobserver variability in their diagnosis. Machine learning has emerged as a powerful tool for research in pathology. To capture the volume of data and manage heterogeneity within the placenta, we developed GestaltNet, which emulates human attention to high-yield areas and aggregation across regions. We used this network to estimate the gestational age (GA) of scanned placental slides and compared it to a baseline model lacking the attention and aggregation functions. In the test set, GestaltNet showed a higher r
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.