no code implementations • 3 May 2023 • Min Cen, Xingyu Li, Bangwei Guo, Jitendra Jonnagaddala, Hong Zhang, Xu Steven Xu
Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status.
no code implementations • 21 Feb 2023 • Min Cen, Xingyu Li, Bangwei Guo, Jitendra Jonnagaddala, Hong Zhang, Xu Steven Xu
However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lack of data regarding NLP models for pathology images.
no code implementations • 23 Aug 2022 • Anran Liu, Xingyu Li, Hongyi Wu, Bangwei Guo, Jitendra Jonnagaddala, Hong Zhang, Xu Steven Xu
Methods We developed an automated, multiscale LinkNet workflow for quantifying cellular-level TILs for CRC tumors using H&E-stained images.
no code implementations • 22 Aug 2022 • Bangwei Guo, Xingyu Li, Jitendra Jonnagaddala, Hong Zhang, Xu Steven Xu
In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin-T), we developed an efficient workflow for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, BRAF, and TP53 mutation) that only required relatively small datasets, but achieved the state-of-the-art (SOTA) predictive performance.
no code implementations • 24 Apr 2022 • Xingyu Li, Jitendra Jonnagaddala, Min Cen, Hong Zhang, Xu Steven Xu
Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs). However, identification of image phenotypes within the WSIs that are relevant to patient survival and disease progression is difficult for both clinicians, and deep learning algorithms.
no code implementations • 5 Nov 2021 • Xingyu Li, Jitendra Jonnagaddala, Shuhua Yang, Hong Zhang, Xu Steven Xu
We developed a novel deep-learning algorithm (CRCNet) using whole-slide images from Molecular and Cellular Oncology (MCO) to predict survival benefit of adjuvant chemotherapy in stage II/III CRC.
1 code implementation • 23 Sep 2020 • Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, Junzhou Huang
We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.
no code implementations • WS 2017 • Dillon C Adam, Jitendra Jonnagaddala, Daniel Han-Chen, Sean Batongbacal, Luan Almeida, Jing Z Zhu, Jenny J Yang, Jumail M Mundekkat, Steven Badman, Abrar Chughtai, C Raina MacIntyre
Effective response to infectious diseases outbreaks relies on the rapid and early detection of those outbreaks.
no code implementations • WS 2017 • Yi-Jie Huang, Chu Hsien Su, Yi-Chun Chang, Tseng-Hsin Ting, Tzu-Yuan Fu, Rou-Min Wang, Hong-Jie Dai, Yung-Chun Chang, Jitendra Jonnagaddala, Wen-Lian Hsu
In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus.