mlsanityData to 14 Oct 2026

VLCD: Vision-Language Contrastive Distillation for Accurate and Efficient Automatic Placenta Analysis

arXiv 2506.02229 ↗First posted 2 Jun 2025cs.CV cs.AI cs.CLcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

Pathological examination of the placenta is an effective method for detecting and mitigating health risks associated with childbirth. Recent advancements in AI have enabled the use of photographs of the placenta and pathology reports for detecting and classifying signs of childbirth-related pathologies. However, existing automated methods are computationally extensive, which limits their deployability. We propose two modifications to vision-language contrastive learning (VLC) frameworks to enhance their accuracy and efficiency: (1) text-anchored vision-language contrastive knowledge distillation (VLCD)-a new knowledge distillation strategy for medical VLC pretraining, and (2) unsupervised predistillation using a large natural images dataset for improved initialization. Our approach distills efficient neural networks that match or surpass the teacher model in performance while achieving model compression and acceleration. Our results showcase the value of unsupervised predistillation in improving the performance and robustness of our approach, specifically for lower-quality images. VLCD serves as an effective way to improve the efficiency and deployability of medical VLC approaches, making AI-based healthcare solutions more accessible, especially in resource-constrained environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 18 of its 32 references that are in the map. First posted on arXiv 2 Jun 2025.

Builds on

18 of 32 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Efficient Medical Image Segmentation Based on Knowledge Distillation23 Aug
  2. 2021Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification7 Jul

Other references · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  5. 2016Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfercited by 1,077
  6. 2021Distilling Knowledge via Knowledge Reviewcited by 288
  7. 2023Learning to Exploit Temporal Structure for Biomedical Vision-Language Processingcited by 211
  8. 2022MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretrainingcited by 187
  9. 2018Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layerscited by 112
  10. 2023Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Trainingcited by 91
  11. 2021Compressing Visual-linguistic Model via Knowledge Distillationcited by 86
  12. 2023Distilling Large Vision-Language Model with Out-of-Distribution Generalizabilitycited by 33
4 more
  1. 2023Improving Knowledge Distillation via Regularizing Feature Norm and Directioncited by 18
  2. 2023Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Trainingcited by 13
  3. 2023Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretrainingcited by 10
  4. 2021Boosting Contrastive Learning with Relation Knowledge Distillationcited by 7

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  3. 2025FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound
  4. 2025Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
  5. 2025PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology
  6. 2025Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning
  7. 2026MedKCO: Medical Vision-Language Pretraining via Knowledge-Driven Cognitive Orchestration
  8. 2025Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling