Author

T. Tan

3 papers indexed here

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Sep 2026

A Deep Learning Model to Predict Breast Cancer Recurrence Using Longitudinal Mammograms and Clinical Data.

Purpose To develop a deep learning model that integrates longitudinal mammograms and static clinical data for predicting the recurrence risk and recurrence subtype of breast cancer. Materials and Methods In this retrospective study, the data of patients examined via imaging from January 2004 to December 2020 were inclu...

Chun-Fang Lu, Xin Wang, Tian-Yu Zhang et al. · Radiology: Artificial Intell... · 0 citations

Open access Aug 2026

MRICombo: a deep-learning-based framework for universal volumetric segmentation grading-staging and malignancy detection across heterogeneous MRI

Comprehensive magnetic resonance imaging (MRI) analysis in oncology involves multiple interrelated tasks including volumetric segmentation, grading, staging, and malignancy detection. However, most existing deep learning models are task-specific or sequence-specific, lacking the generalizability required for heterogene...

Zhuo-Neng Zhang, Lu-Yi Han, Deng-Qiang Jia et al. · Nature Communications · 2 citations · ⚡1

#artificial intelligence Preprint Aug 2026

Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V

TRIAGE, Tracer-aware Refinement via Interactive Anatomy-Guided sEgmentation, a 3D STU-Net initialized through masked autoencoding pre-training with an asynchronous masking strategy, aiming to learn transferable anatomical and cross-modal representations before task-specific fine-tuning.

Xing-Long Liang, Chun-Fang Lu, Tian-Yu Zhang et al. · 0 citations