8 citations · 12 across the 4 of their papers we have counts for
7 papers
Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net
Hamed Fayyaz, Ehsan Kozegar, Tao Tan +1
Automated 3-D breast ultrasound (ABUS) is a newfound system for breast screening that has been proposed as a supplementary modality to mammography for breast cancer detection. Whil…
Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19
Tao Tan, Bipul Das, Ravi Soni +13
The COVID-19 pandemic continues to spread and impact the well-being of the global population. The front-line modalities including computed tomography (CT) and X-ray play an importa…
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
Zhang Li, Jiehua Zhang, Tao Tan +30
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in…
Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets
Jie Xing, Zheren Li, Biyuan Wang +6
Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the b…
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Zhang Li, Zheyu Hu, Jiaolong Xu +10
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform be…
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
Tao Tan, Zhang Li, Haixia Liu +15
Bronchoscopy inspection as a follow-up procedure from the radiological imaging plays a key role in lung disease diagnosis and determining treatment plans for the patients. Doctors…