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20182026
most citedESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation

13 citations · 25 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation

Elijah Danquah Darko, Min Xian, Terence Soule +2

Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks or rely on passive refinement schemes that…

cs.CV2021

BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images

Boyu Zhang, Aleksandar Vakanski, Min Xian

In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This paper introduces BI-RADS-Net,…

cs.CV2020

A Review of Computational Approaches for Evaluation of Rehabilitation Exercises

Yalin Liao, Aleksandar Vakanski, Min Xian +2

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the hea…

cs.CV2019

Breast Anatomy Enriched Tumor Saliency Estimation

Fei Xu, Yingtao Zhang, Min Xian +5

Breast cancer investigation is of great significance, and developing tumor detection methodologies is a critical need. However, it is a challenging task for breast ultrasound due t…

cs.CV20196 cited

Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling

Fei Xu, Yingtao Zhang, Min Xian +5

Tumor saliency estimation aims to localize tumors by modeling the visual stimuli in medical images. However, it is a challenging task for breast ultrasound due to the complicated a…

cs.CV2018

A Hybrid Framework for Tumor Saliency Estimation

Fei Xu, Min Xian, Yingtao Zhang +6

Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality. Most tumor segmentati…