PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation

no code implementations • 24 Mar 2024 • Yijing Yang, Vasileios Magoulianitis, Jiaxin Yang, Jintang Xue, Masatomo Kaneko, Giovanni Cacciamani, Andre Abreu, Vinay Duddalwar, C. -C. Jay Kuo, Inderbir S. Gill, Chrysostomos Nikias

Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning.

3D Prostate Segmentation Representation Learning +1

PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation

no code implementations • 24 Mar 2024 • Vasileios Magoulianitis, Jiaxin Yang, Yijing Yang, Jintang Xue, Masatomo Kaneko, Giovanni Cacciamani, Andre Abreu, Vinay Duddalwar, C. -C. Jay Kuo, Inderbir S. Gill, Chrysostomos Nikias

Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity.

Bibliometric Analysis of Publisher and Journal Instructions to Authors on Generative-AI in Academic and Scientific Publishing

no code implementations • 21 Jul 2023 • Conner Ganjavi, Michael B. Eppler, Asli Pekcan, Brett Biedermann, Andre Abreu, Gary S. Collins, Inderbir S. Gill, Giovanni E. Cacciamani

Among the largest 100 publishers, 17% provided guidance on the use of GAI, of which 12 (70. 6%) were among the top 25 publishers.

Development of the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines

no code implementations • 18 Jul 2023 • Giovanni E. Cacciamani, Michael B. Eppler, Conner Ganjavi, Asli Pekan, Brett Biedermann, Gary S. Collins, Inderbir S. Gill

The swift progress and ubiquitous adoption of Generative AI (GAI), Generative Pre-trained Transformers (GPTs), and large language models (LLMs) like ChatGPT, have spurred queries about their ethical application, use, and disclosure in scholarly research and scientific productions.