43 citations · 47 across the 5 of their papers we have counts for
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Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
Peter D. Erickson, Tejas Sudharshan Mathai, Ronald M. Summers
Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion detection algorithms have been devel…
Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
Tejas Sudharshan Mathai, Sungwon Lee, Thomas C. Shen +2
Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distingu…
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation
Boah Kim, Yujin Oh, Bradford J. Wood +2
Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in i…
Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision
Seung Yeon Shin, Thomas C. Shen, Ronald M. Summers
We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesio…
Universal Lymph Node Detection in T2 MRI using Neural Networks
Tejas Sudharshan Mathai, Sungwon Lee, Thomas C. Shen +2
Purpose: Identification of abdominal Lymph Nodes (LN) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) scans is critical for staging of lymphoproliferative…