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J. Song

4 papers hereh-index 25 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • eess.IV1
same name
  • J. Song — 146 papers, h 14
  • J. Song — 74 papers, h 9
  • J. Song — 39 papers, h 9
  • J. Song — 27 papers, h 8
  • J. Song — 10 papers, h 5
  • J. Song — 5 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.CV2026

TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis

Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song +3

Whole slide image (WSI) analysis is central to computational pathology, with multiple instance learning (MIL) emerging as the standard pipeline for slide-level diagnosis. However,…

cs.CV2026

Hierarchical Classification for Improved Histopathology Image Analysis

Keunho Byeon, Jinsol Song, Seong Min Hong +2

Whole-slide image analysis is essential for diagnostic tasks in pathology, yet existing deep learning methods primarily rely on flat classification, ignoring hierarchical relations…

cs.CV2025

Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images

Jinsol Song, Jiamu Wang, Anh Tien Nguyen +4

Anomaly detection in computational pathology aims to identify rare and scarce anomalies where disease-related data are often limited or missing. Existing anomaly detection methods,…

eess.IV2025

Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis

Jiamu Wang, Keunho Byeon, Jinsol Song +4

Anomaly detection is an emerging approach in digital pathology for its ability to efficiently and effectively utilize data for disease diagnosis. While supervised learning approach…

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