Sen Yang

Orcid: 0000-0002-0639-4122

Affiliations:
  • Stanford University, Department of Radiation Oncology, CA, USA
  • Tencent AI Laboratory, Shenzhen, China
  • Sichuan University, College of Biomedical Engineering, Chengdu, China


According to our database1, Sen Yang authored at least 38 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification.
IEEE Trans. Medical Imaging, December, 2024

SAC-Net: Enhancing Spatiotemporal Aggregation in Cervical Histological Image Classification via Label-Efficient Weakly Supervised Learning.
IEEE Trans. Circuits Syst. Video Technol., August, 2024

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.
Medical Image Anal., February, 2024

Domain generalization across tumor types, laboratories, and species - Insights from the 2022 edition of the Mitosis Domain Generalization Challenge.
Medical Image Anal., 2024

Artificial Intelligence-Enhanced Couinaud Segmentation for Precision Liver Cancer Therapy.
CoRR, 2024

Mesoscopic structure graphs for interpreting uncertainty in non-linear embeddings.
Comput. Biol. Medicine, 2024

2023
PAIP 2020: Microsatellite instability prediction in colorectal cancer.
Medical Image Anal., October, 2023

CLC-Net: Contextual and local collaborative network for lesion segmentation in diabetic retinopathy images.
Neurocomputing, March, 2023

Merging nucleus datasets by correlation-based cross-training.
Medical Image Anal., 2023

A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images.
Medical Image Anal., 2023

RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval.
Medical Image Anal., 2023

Mitosis domain generalization in histopathology images - The MIDOG challenge.
Medical Image Anal., 2023

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting.
CoRR, 2023

Federated contrastive learning models for prostate cancer diagnosis and Gleason grading.
CoRR, 2023

Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images.
Comput. Biol. Medicine, 2023

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.
Genom. Proteom. Bioinform., October, 2022

Knowledge-Based Representation Learning for Nucleus Instance Classification From Histopathological Images.
IEEE Trans. Medical Imaging, 2022

Automatic Segmentation of Pneumothorax in Chest Radiographs Based on a Two-Stage Deep Learning Method.
IEEE Trans. Cogn. Dev. Syst., 2022

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge.
Medical Image Anal., 2022

Transformer-based unsupervised contrastive learning for histopathological image classification.
Medical Image Anal., 2022

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.
Medical Image Anal., 2022

Mitosis domain generalization in histopathology images - The MIDOG challenge.
CoRR, 2022

Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning.
CoRR, 2022

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines.
CoRR, 2022

Automated segmentation of normal and diseased coronary arteries - The ASOCA challenge.
Comput. Medical Imaging Graph., 2022

SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images.
Medical Image Anal., 2021

PAIP 2019: Liver cancer segmentation challenge.
Medical Image Anal., 2021

Sk-Unet Model with Fourier Domain for Mitosis Detection.
Proceedings of the Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021

TransPath: Transformer-Based Self-supervised Learning for Histopathological Image Classification.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2020
A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI.
Proceedings of the Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, 2020

Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Automatic Glioma Grading Based on Two-Stage Networks by Integrating Pathology and MRI Images.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019
HypernasalityNet: Deep recurrent neural network for automatic hypernasality detection.
Int. J. Medical Informatics, 2019

Automatic Hypernasality Detection in Cleft Palate Speech Using CNN.
Circuits Syst. Signal Process., 2019

SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR.
Proceedings of the Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 2019


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