Jue Jiang

Spatiotemporal Modeling of Tumor Dynamics via Optimal Transport for Enhanced Longitudinal Analysis

Jiannan Yang, Jue Jiang, Tengfei Ma, Veeraraghavan Harini

us (code pays fourni par la source)

Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion

Jorge Tapias Gomez, Nishant Nadkarni, Lando S Bosma, Jue Jiang et autres

Abstract Objective . Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DTs) modeling temporally varying motion …

us, nl (code pays fourni par la source)

Anti-colorectal cancer bioactive metabolites from endophytic fungi of Pyrola spp. targeting p-ATM/P53 pathway driven by in vitro experiments and network pharmacology

Xiliang Yang, Qianqian Bao, Dongyu Hu, Jingyang Zhou et autres

cn (code pays fourni par la source)

Topology-aware multi-view hypergraph computation-based cross-modal brain network fusion for brain disease diagnosis

Shaoyi Du, Heming Xu, Rundong Xue, Xiangmin Han et autres

cn (code pays fourni par la source)

Multi-level graph self-supervised learning for multi-modal medical corpus construction

Yuping Lin, Xudong Chen, Rundong Xue, Jue Jiang et autres

cn (code pays fourni par la source)

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy

Sudharsan Madhavan, Chengcheng Gui, Lando S Bosma, Josiah Simeth et autres

Background: Accurate deformable image registration (DIR) is required for contour propagation and dose accumulation in MR-guided adaptive radiotherapy (MRgART). This study trained and evaluated a deep learning DIR method for domain invariant MR-MR registration. Methods: A progressively refined registration and segmentation (ProRSeg) …

Large language model trained on clinical oncology data predicts cancer progression

Menglei Zhu, Hui Lin, Jue Jiang, Abbas J. Jinia et autres

Subspecialty knowledge barriers have limited the adoption of large language models (LLMs) in oncology. We introduce Woollie, an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung, breast, prostate, pancreatic, and colorectal cancers, with external …

us (code pays fourni par la source)

Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion

Jorge Tapias Gomez, Nishant Nadkarni, Lando S Bosma, Jue Jiang et autres

Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DT) modeling temporally varying motion were created …

Explainable Modeling Based on Prior Knowledge Embedding and Higher-Order Correlations

Dong Zhang, Shuai-Chen Zhuo, Yuan Sun, Xi-Jing Wang et autres

cn (code pays fourni par la source)

DCATNet: polyp segmentation with deformable convolution and contextual-aware attention network

Zenan Wang, Tianshu Li, Ming Liu, Jue Jiang et autres

Polyp segmentation is crucial in computer-aided diagnosis but remains challenging due to the complexity of medical images and anatomical variations. Current state-of-the-art methods struggle with accurate polyp segmentation due to the variability in size, shape, and texture. These factors make boundary detection …

cn, us (code pays fourni par la source)

Benchmarking Transferability of Self-Supervised Pretraining for Multi-Organ Segmentation on Different Modalities

Jue Jiang, Harini Veeraraghavan

Self-supervised learning (SSL) is an approach to pretrain deep networks with unlabeled datasets by using pretext tasks that use images as "ground truth". Pretext tasks have been shown to impact accuracy of task categories, e.g. segmentation vs. classification. However, versatility of SSL …

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Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets

Aneesh Rangnekar, Nishant Nadkarni, Jue Jiang, Harini Veeraraghavan

Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution (ID) datasets. However, reliable performance on ID datasets does not guarantee robust generalization on out-of-distribution (OOD) datasets. Importantly, …

us (code pays fourni par la source)