Multimodal Knowledge Distillation for Gastric Adenocarcinoma Classification from Whole-Slide Images

Key points

• Gastric adenocarcinoma (GA) is a leading cause of cancer-related mortality worldwide, and accurate histopathological subtype classification from whole-slide images (WSIs) is essential for effective treatment planning.1
• While multimodal approaches that integrate pathology report text with WSIs can improve classification, existing methods often depend on computationally expensive transformer architectures and large language models.1
• The teacher model learns fused image-text representations for subtype classification, while the student model distills this knowledge to enable accurate image-only inference.1
• We evaluate our method on the PatchGastric benchmark dataset and achieve at least 3.35% higher mean accuracy than state-of-the-art approaches, without relying on transformer-based fusion, multi-task learning, or large language models.1

Sources (1)

• [1]Multimodal Knowledge Distillation for Gastric Adenocarcinoma Classification from Whole-Slide Images
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 07:56 AM

  We propose a multimodal knowledge distillation (MKD) framework that combines a pretrained WSI image encoder and a clinical text encoder using Low-Rank Multimodal Fusion (LMF) to efficiently model cross-modal interactions during training.

  Gastric adenocarcinoma (GA) is a leading cause of cancer-related mortality worldwide, and accurate histopathological subtype classification from whole-slide images (WSIs) is essential for effective treatment planning.

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