Le Hou    Stony Brook University

About Me   

- I like board games, dota 2, and computer vision.
- I worked at Baidu, did a summer internship at Google and Microsoft research.
- Now I'm a PhD candidate at Stony Brook University.
- Research of interests: machine learning, deep learning, medical image analysis.
- Visit our lab: Computer Vision Lab


lehou0312 [at] gmail.com



I am interested in machine learning techniques especially artificial neural networks. A central interest is to develop and improve convolutional neural network based methods for medical image analysis, image segmentation, human age classification, etc.


- L. Hou, Y. Cheng, N. Shazeer, N. Parmar, Y. Li, P. Korfiatis, T.M. Drucker, D.J. Blezek, X. Song. High Resolution Medical Image Analysis with Spatial Partitioning. NeurIPS workshop on Medical Imaging meets NeurIPS. 2019.
- L. Hou, T.F.Y. Vicente, M. Hoai, D. Samaras. Large scale shadow annotation and detection using lazy annotation and stacked CNNs. PAMI 2019. SBU shadow dataset
- L. Hou, A. Agarwal, D. Samaras, T.M. Kurc, R.R. Gupta, J.H. Saltz. Robust Histopathology Image Analysis: to Label or to Synthesize? Oral in CVPR. 2019. code and data
- C. Robinson, L. Hou, K. Malkin, R. Soobitsky, J. Czawlytko, B. Dilkina, N. Jojic. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. CVPR. 2019.
- K. Malkin, C. Robinson, Le Hou, R. Soobitsky, J. Czawlytko, D. Samaras, J. Saltz, L. Joppa, N. Jojic. Label super-resolution networks. ICLR. 2019
- L. Hou, V. Nguyen, A.B. Kanevsky, D. Samaras, T.M. Kurc, T. Zhao, R.R. Gupta, Y. Gao, W. Chen, D. Foran, J.H. Saltz. Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images. Pattern Recognition. 2018. individual lymphocyte classification dataset and code and data (released under another paper) and a pytorch implementation (Thanks to Mihir Sahasrabudhe)
- J.H. Saltz, R.R. Gupta, L. Hou, ..., V. Thorsson. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports. 2018. paper and code
- L. Hou, C.P. Yu, D. Samaras. Squared Earth Mover's Distance Loss for Training Deep Neural Networks on Ordered-Classes. NeurIPS workshop on Learning on Distributions, Functions, Graphs and Groups. 2017. code
- L. Hou, D. Samaras, T.M. Kurc, Y. Gao, J.H. Saltz. ConvNets with Smooth Adaptive Activation Functions for Regression. AISTATS. 2017. code
- V. Murthy, L. Hou, D. Samaras, T.M. Kurc, J.H. Saltz. Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images. WACV. 2017. nucleus classification dataset
- T.F.Y. Vicente, L. Hou, C.P. Yu, M. Hoai, D. Samaras. Large-scale training of shadow detectors with noisily-annotated shadow examples. ECCV. 2016. SBU shadow dataset
- L. Hou, D. Samaras, T. M. Kurc, Y. Gao, J. E. Davis, and J. H. Saltz. Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification. Spotlight in CVPR. 2016.

- T. Zhao, L. Hou, V. Nguyen, Y. Gao, D. Samaras, T.M. Kurc, J. Saltz. Using Machine Methods to Score Tumor-Infiltrating Lymphocytes in Lung Cancer. Abstract in The United States and Canadian Academy of Pathology (USCAP). 2017.
- L. Hou, K. Singh, D. Samaras, T. Kurc, Y. Gao, R. Seidman, J. Saltz, Automatic Histopathology Image Analysis with CNNs. Abstract in New York Scientific Data Summit. 2016.
- M. Gardner, L. Hou, D. Samaras, A. Fontanini. Development of an Automated Method for Analysis of Mouth Movements and Orofacial Reactions in Restrained Rats. Abstract in Society for Neuroscience. 2014.

- L. Hou, C.P. Yu, D. Samaras. Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks. arXiv. 2016.
- L. Hou, M. Gardner, D. Samaras, A. Fontanini. Rats' Orofacial Activity Recognition and Its Applications. Master's thesis. 2014.