54 citations · 108 across the 9 of their papers we have counts for
11 papers
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…
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering
Xinyue Hu, Lin Gu, Qiyuan An +6
To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair of main and reference ima…
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
Qingqing Zhu, Tejas Sudharshan Mathai, Pritam Mukherjee +3
Despite the reduction in turn-around times in radiology reports with the use of speech recognition software, persistent communication errors can significantly impact the interpreta…
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Gregory Holste, Song Wang, Ziyu Jiang +5
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual pre…
Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor
Seung Yeon Shin, Thomas C. Shen, Stephen A. Wank +1
Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation…
Graph-Based Small Bowel Path Tracking with Cylindrical Constraints
Seung Yeon Shin, Sungwon Lee, Ronald M. Summers
We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the…
Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations
Seung Yeon Shin, Ronald M. Summers
Small bowel path tracking is a challenging problem considering its many folds and contact along its course. For the same reason, it is very costly to achieve the ground-truth (GT)…
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…