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20172023
most citedWhole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

523 citations · 658 across the 15 of their papers we have counts for

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Showing eess.IVShow all

11 papers · 1 filter

eess.IV2023★ 1 cited

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

Hexin Dong, Jiawen Yao, Yuxing Tang +11

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients.…

eess.IV2023★ 1 cited

Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

Mingze Yuan, Yingda Xia, Xin Chen +10

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, an…

eess.IV2023★ 1 cited

Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

Ke Yan, Xiaoli Yin, Yingda Xia +10

Liver tumor segmentation and classification are important tasks in computer aided diagnosis. We aim to address three problems: liver tumor screening and preliminary diagnosis in no…

eess.IV2023★ 1 cited

Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT

Bo Zhou, Yingda Xia, Jiawen Yao +5

Pancreatic cancer is one of the leading causes of cancer-related death. Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i.e…

eess.IV2023★ 1 cited

A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging

Zhilin Zheng, Xu Fang, Jiawen Yao +10

Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in gene…

eess.IV2023★ 1 cited

CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans

Jieneng Chen, Yingda Xia, Jiawen Yao +22

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus o…

eess.IV2020

3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management

Tianyi Zhao, Kai Cao, Jiawen Yao +6

The pancreatic disease taxonomy includes ten types of masses (tumors or cysts)[20,8]. Previous work focuses on developing segmentation or classification methods only for certain ma…

eess.IV2020★ 523 cited

Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala +2

Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have show…

eess.IV2020

DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging

Jiawen Yao, Yu Shi, Le Lu +2

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. Surgery remains the best chance of a potential cure for patients who are e…

eess.IV2020★ 5 cited

Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans

Ling Zhang, Yu Shi, Jiawen Yao +5

Accurate and automated tumor segmentation is highly desired since it has the great potential to increase the efficiency and reproducibility of computing more complete tumor measure…

eess.IV2019★ 1 cited

CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

Bo Zhou, Adam P. Harrison, Jiawen Yao +4

As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data w…