Area of research
Artificial Intelligence · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Smart Agriculture and AI, and Digital Imaging for Blood Diseases.
Pancancer outcome prediction via a unified weakly supervised deep learning model
A Joint Detection Network for Snowy Weather with Image Recovery and Domain Adaptation
A pathology foundation model for cancer diagnosis and prognosis prediction
Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge
HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
Intermediate Domain Meets Natural Hazy Tracking
An end-to-end algorithm for predicting missing load data based on waveGAIN
CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting
SAC-Net: Enhancing Spatiotemporal Aggregation in Cervical Histological Image Classification via Label-Efficient Weakly Supervised Learning
The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading
HMT-Net: Transformer and MLP Hybrid Encoder for Skin Disease Segmentation
Transformer-based unsupervised contrastive learning for histopathological image classification
Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge
Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images
OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines
Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution