LLM360: Towards Fully Transparent Open-Source LLMs

1 code implementation • 11 Dec 2023 • Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing

The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.

Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment

no code implementations • 1 Dec 2016 • Imon Banerjee, Lewis Hahn, Geoffrey Sonn, Richard Fan, Daniel L. Rubin

We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC).

Deep Learning for Prostate Pathology

no code implementations • 11 Oct 2019 • Okyaz Eminaga, Yuri Tolkach, Christian Kunder, Mahmood Abbas, Ryan Han, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Sebastian Huss, Andreas Loening, Robert West, Geoffrey Sonn, Richard Fan, Olaf Bettendorf, James Brook, Daniel Rubin

For case usage, these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens.

Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration

no code implementations • 13 Jul 2022 • Ziyi Shen, Qianye Yang, Yuming Shen, Francesco Giganti, Vasilis Stavrinides, Richard Fan, Caroline Moore, Mirabela Rusu, Geoffrey Sonn, Philip Torr, Dean Barratt, Yipeng Hu

Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients.

Image Registration Quantization

Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathology

no code implementations • 23 Aug 2023 • Okyaz Eminaga, Mahmoud Abbas, Christian Kunder, Yuri Tolkach, Ryan Han, James D. Brooks, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Robert West, Jin Long, Richard Fan, Olaf Bettendorf

Adjusting the decision threshold for the secondary Gleason pattern from 5% to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (kappa from 0. 44 to 0. 64).