Classification of Whole Slide Imagesfor the Presence of Maternal Vascular Malperfusion Lesions Using Attention-Based, Weakly Supervised Deep Learning
Bibliographic record
Abstract
Placental lesions indicative of maternal vascular malperfusion (MVM) are associated with future cardiovascular disease (CVD) in women with placenta-mediated diseases of pregnancy. Early diagnosis of CVD can reduce morbidity, mortality, and healthcare costs. MVM lesions can be detected in placental histopathology images, providing a means for CVD risk screening postnatally. Deep learning approaches, such as convolutional neural networks (CNNs), have demonstrated high potential for automating histopathology image analysis. Given the large size of histopathology whole slide images (WSIs), a patch-based approach is often employed; however, labeling is typically only available at the WSI level. MVM lesions can present heterogeneously across the image, so assigning WSI labels to patches results in patch mislabeling. In this study, we propose a weakly supervised learning method for MVM lesion classification. Features were computed from the patches extracted from WSIs using the CNN-based Resnet18 pre-trained model. An attention-based network, using weakly supervised learning, has been developed to classify WSIs as MVM+/-. The model performance was assessed against a baseline model, which was a patch-based fully supervised model. The weakly supervised learning method had an accuracy of 87.4%, which was superior to the baseline model accuracy of 81.1%. This is a promising result that suggests that weakly supervised learning can help overcome patch labeling errors that arise from the heterogeneous presentation of histopathology features, such as MVM lesions, and only having WSI-level labels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".