activity
20162024
most citedImage quality assessment for machine learning tasks using meta-reinforcement learning

51 citations · 76 across the 18 of their papers we have counts for

collaborators

18 papers

eess.IV2024

Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound

Lichun Zhang, Steve Ran Zhou, Moon Hyung Choi +14

Prostate cancer is a leading cause of cancer-related deaths among men. The recent development of high frequency, micro-ultrasound imaging offers improved resolution compared to con…

cs.RO2024★ 1 cited

Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors

Julia Di, Zdravko Dugonjic, Will Fu +11

Vision-based tactile sensors have recently become popular due to their combination of low cost, very high spatial resolution, and ease of integration using widely available miniatu…

eess.IV2023★ 2 cited

ProsDectNet: Bridging the Gap in Prostate Cancer Detection via Transrectal B-mode Ultrasound Imaging

Sulaiman Vesal, Indrani Bhattacharya, Hassan Jahanandish +8

Interpreting traditional B-mode ultrasound images can be challenging due to image artifacts (e.g., shadowing, speckle), leading to low sensitivity and limited diagnostic accuracy.…

eess.IV2022★ 1 cited

Correlated Feature Aggregation by Region Helps Distinguish Aggressive from Indolent Clear Cell Renal Cell Carcinoma Subtypes on CT

Karin Stacke, Indrani Bhattacharya, Justin R. Tse +3

Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Indolent RCC is often low-grade without necrosis and can be monitored without treatment. Aggressive…

eess.IV2022★ 2 cited

Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study

Sulaiman Vesal, Iani Gayo, Indrani Bhattacharya +7

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies…

eess.IV2022

Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration

Ziyi Shen, Qianye Yang, Yuming Shen +9

Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients. This paper describes a development in improving the lear…