Hello, I'm Anh Tien Nguyen

I am a graduate Master’s student at Korea University, supervised by Prof. Jin Tae Kwak. My works focus on medical image analysis, especially computational pathology. Recently, I am working on whole slide image analysis and interpretable methods in computational pathology. Email: ngtienanh (at) korea (dot) ac (dot) kr

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News

  • 03/2025: VLEER, an explainable framework for WSI representation, is released.
  • 02/2025: 2DMamba is accepted at CVPR 2025.
  • 02/2025: I obtained the Master of Science from Korea University, Seoul, South Korea.
  • 12/2024: 2DMamba, a 2D Mamba-based method for vision taks, has been released.
  • 11/2024: Defensed my Master’s thesis.
  • 10/2024: Ranked 6th in MICCAI 2024-LEOPARD for predicting biochemical recurrence of prostate cancer.
  • 08/2024: Received the KU Foreign Global Leader Scholarship for excellent GPA, research projects, and publications.
  • 05/2024: TQx has been accepted at MICCAI 2024 (Early accept, top 11%).
  • 03/2024: Received the BK21 Scholarship for excellent research projects and publications.
  • 09/2023: GPC has been accepted to MICCAI-MedAGI 2023 with “Best Paper Honorable Mention”.

Publications

VLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representation

VLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representation

Anh Tien Nguyen*, Keunho Byeon, Kyungeun Kim, Jin Tae Kwak
Under review

A novel method designed to leverage VLMs for WSI representation with explainability

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2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification

2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification

Jingwei Zhang*, Anh Tien Nguyen*, Xi Han*, Vincent Quoc-Huy Trinh, Hong Qin, Dimitris Samaras, Mahdi S. Hosseini
CVPR 2025

A novel 2D selective SSM framework that incorporates the 2D spatial structure of images into Mamba, with a highly optimized hardware-aware operator.

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Towards a text-based quantitative and explainable histopathology image analysis

Towards a text-based quantitative and explainable histopathology image analysis

Anh Tien Nguyen*, Trinh Thi Le Vuong, Jin Tae Kwak
MICCAI 2024 (Early accept, top 11%)

An explainable method to utilize texts for quantifying pathology images

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CAMP: Continuous and Adaptive Learning Model in Pathology

CAMP: Continuous and Adaptive Learning Model in Pathology

Anh Tien Nguyen*, Keunho Byeon, Kyungeun Kim, Boram Song, Seoung Wan, Jin Tae Kwak
Under review (journal)

A framework for continuous learning that is applicable for any vision model.

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GPC: Generative and General Pathology Image Classifier

GPC: Generative and General Pathology Image Classifier

Anh Tien Nguyen*, Jin Tae Kwak
MICCAI-MedAGI 2023 (Best Paper Honorable Mention)

A task-agnostic generative and general pathology image classifier

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