Mingquan LIN

Assistant Professor

University of Minnesota

United States

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Dr. Mingquan Lin is an Assistant Professor at the University of Minnesota, USA. His research focuses on advanced methodologies in medical imaging and disease classification, particularly in areas such as long-tailed disease classification from chest X-rays and multimodal survival analysis in breast cancer. He has published extensively on topics including adversarial training in medical image segmentation and the application of deep learning techniques to improve predictive accuracy in healthcare.

Mingquan LIN

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Articles (16)

Foundation-model–based multimodal transformer framework for survival analysis in HER2-stratified breast cancer

CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray

Adversarial training with misaligned label correction for carotid segmentation from simultaneous non-contrast angiography and intraplaque hemorrhage MRI

A multimodal approach for few-shot biomedical named entity recognition in low-resource languages

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

Author Correction: Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Deep learning with noisy labels in medical prediction problems: a scoping review

Multiclass and Multilabel Classifications by Consensus and Complementarity-Based Multiview Latent Space Projection

Deep learning models to predict primary open‐angle glaucoma

An empirical study of using radiology reports and images to improve intensive care unit mortality prediction

Improving Fairness of Automated Chest Radiograph Diagnosis by Contrastive Learning

Improving model fairness in image-based computer-aided diagnosis

Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access

A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module

Multimodal Image Classification by Multiview Latent Pattern Extraction, Selection, and Correlation

Collaborators (4)

Lei Qiu

Bernard Chiu

Hongmei Jiang

Yuzhe Yang

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