no code implementations • 23 Apr 2024 • Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long-Kai Huang, Jianhua Yao, Wei Liu, Yu Rong
We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels.
no code implementations • 18 Apr 2024 • Chaohao Yuan, Songyou Li, Geyan Ye, Yikun Zhang, Long-Kai Huang, Wenbing Huang, Wei Liu, Jianhua Yao, Yu Rong
The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions.
no code implementations • 7 Feb 2024 • Qingyu Yin, Xuzheng He, Xiang Zhuang, Yu Zhao, Jianhua Yao, Xiaoyu Shen, Qiang Zhang
The decoder-only Transformer architecture with causal masking and relative position encoding (RPE) has become the de facto choice in language modeling.
no code implementations • 14 Dec 2023 • Wentao Pan, Zhe Xu, Jiangpeng Yan, Zihan Wu, Raymond Kai-yu Tong, Xiu Li, Jianhua Yao
Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning, wherein the weak-to-strong consistency regularization framework, popularized by FixMatch, is widely used as a benchmark scheme.
1 code implementation • 29 Jul 2023 • Mingcai Chen, Yu Zhao, Zhonghuang Wang, Bing He, Jianhua Yao
Immune repertoire classification, a typical multiple instance learning (MIL) problem, is a frontier research topic in computational biology that makes transformative contributions to new vaccines and immune therapies.
no code implementations • 11 Jul 2023 • Daoan Zhang, Weitong Zhang, Yu Zhao, JianGuo Zhang, Bing He, Chenchen Qin, Jianhua Yao
Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge.
no code implementations • 9 Apr 2023 • Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, QinGhua Hu, Bingzhe Wu, Changqing Zhang, Jianhua Yao
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions.
1 code implementation • 26 Nov 2022 • Wentao Pan, Jiangpeng Yan, Hanbo Chen, Jiawei Yang, Zhe Xu, Xiu Li, Jianhua Yao
Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary.
no code implementations • 16 Nov 2022 • Mingcai Chen, Yu Zhao, Bing He, Zongbo Han, Bingzhe Wu, Jianhua Yao
Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.
1 code implementation • 19 Sep 2022 • Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
no code implementations • 6 Aug 2022 • Zhikang Wang, Yue Bi, Tong Pan, Xiaoyu Wang, Chris Bain, Richard Bassed, Seiya Imoto, Jianhua Yao, Jiangning Song
Multiple instance learning (MIL) is a powerful approach to classify whole slide images (WSIs) for diagnostic pathology.
1 code implementation • 14 Jul 2022 • Jiawei Yang, Hanbo Chen, Yuan Liang, Junzhou Huang, Lei He, Jianhua Yao
We first benchmark representative SSL methods for dense prediction tasks in pathology images.
1 code implementation • 5 Jul 2022 • Jiawei Yang, Hanbo Chen, Yu Zhao, Fan Yang, Yao Zhang, Lei He, Jianhua Yao
We evaluate ReMix on two public datasets with two state-of-the-art MIL methods.
no code implementations • 15 Jun 2022 • Jiangpeng Yan, Chenghui Yu, Hanbo Chen, Zhe Xu, Junzhou Huang, Xiu Li, Jianhua Yao
Four different implementations of anatomy-specific learners are presented and explored on the top of our framework in two MRI reconstruction networks.
no code implementations • 14 Apr 2022 • Jianye Pang, Cheng Jiang, Yihao Chen, Jianbo Chang, Ming Feng, Renzhi Wang, Jianhua Yao
Therefore, designing an elegant and efficient vision transformer learner for dense prediction in medical volume is promising and challenging.
1 code implementation • 18 Feb 2022 • Jiawei Yang, Hanbo Chen, Jiangpeng Yan, Xiaoyu Chen, Jianhua Yao
Histology images are a natural choice for such a study.
1 code implementation • CVPR 2022 • Zongbo Han, Fan Yang, Junzhou Huang, Changqing Zhang, Jianhua Yao
To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification.
no code implementations • ICLR 2022 • Jiawei Yang, Hanbo Chen, Jiangpeng Yan, Xiaoyu Chen, Jianhua Yao
Histology images are a natural choice for such study.
no code implementations • 24 Nov 2020 • Cheng Jiang, Jun Liao, Pei Dong, Zhaoxuan Ma, De Cai, Guoan Zheng, Yueping Liu, Hong Bu, Jianhua Yao
Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency.
no code implementations • 15 Sep 2020 • Jun Zhang, Kuan Tian, Pei Dong, Haocheng Shen, Kezhou Yan, Jianhua Yao, Junzhou Huang, Xiao Han
Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem.
1 code implementation • 30 Apr 2020 • Yifan Zhang, Shuaicheng Niu, Zhen Qiu, Ying WEI, Peilin Zhao, Jianhua Yao, Junzhou Huang, Qingyao Wu, Mingkui Tan
There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.
no code implementations • 23 Feb 2019 • Ling Zhang, Le Lu, Xiaosong Wang, Robert M. Zhu, Mohammadhadi Bagheri, Ronald M. Summers, Jianhua Yao
Results validate that the ST-ConvLSTM produces a Dice score of 83. 2%+-5. 1% and a RVD of 11. 2%+-10. 8%, both significantly outperforming (p<0. 05) other compared methods of linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes.
no code implementations • 14 Oct 2018 • Haoming Lin, Yuyang Hu, Siping Chen, Jianhua Yao, Ling Zhang
However, CNN in previous studies do not involve cell morphological information, and it is unknown whether morphological features can be directly modeled by CNN to classify cervical cells.
no code implementations • 25 Jan 2018 • Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao
However, the success of most traditional classification methods relies on the presence of accurate cell segmentations.
no code implementations • 25 Jan 2018 • Ling Zhang, Vissagan Gopalakrishnan, Le Lu, Ronald M. Summers, Joel Moss, Jianhua Yao
In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data.
no code implementations • 25 Jan 2018 • Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics.
no code implementations • 25 Jan 2018 • Zhihui Guo, Ling Zhang, Le Lu, Mohammadhadi Bagheri, Ronald M. Summers, Milan Sonka, Jianhua Yao
The cost for each node of the graph is determined by the UNet probability maps.
no code implementations • 1 Jun 2017 • Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao
Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor.
no code implementations • 23 Jan 2017 • Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Mohammadhadi Bagheri, Isabella Nogues, Jianhua Yao, Ronald M. Summers
The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
1 code implementation • CVPR 2016 • Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
no code implementations • 25 Mar 2016 • Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Isabella Nogues, Jianhua Yao, Ronald Summers
Obtaining semantic labels on a large scale radiology image database (215, 786 key images from 61, 845 unique patients) is a prerequisite yet bottleneck to train highly effective deep convolutional neural network (CNN) models for image recognition.
no code implementations • 10 Feb 2016 • Hoo-chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M. Summers
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
no code implementations • 1 Feb 2016 • Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92. 5 $\pm$ 3. 1% to 93. 8 $\pm$ 2. 1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0. 75 $\pm$ 0. 60mm and 8. 63 $\pm$ 4. 44mm to 0. 30 $\pm$ 0. 27mm and 3. 65 $\pm$ 2. 87mm, respectively.
no code implementations • 29 Jan 2016 • Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers
In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.
no code implementations • 27 Jan 2016 • Yinong Wang, Jianhua Yao, Joseph E. Burns, Ronald M. Summers
Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment.
no code implementations • 13 Jan 2016 • Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies.
no code implementations • CVPR 2015 • Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system.
no code implementations • 12 May 2015 • Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, Ronald M. Summers
By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.
no code implementations • 4 May 2015 • Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.
1 code implementation • 15 Apr 2015 • Holger R. Roth, Christopher T. Lee, Hoo-chang Shin, Ari Seff, Lauren Kim, Jianhua Yao, Le Lu, Ronald M. Summers
We show that a data augmentation approach can help to enrich the data set and improve classification performance.
no code implementations • 22 Jul 2014 • Holger R. Roth, Jianhua Yao, Le Lu, James Stieger, Joseph E. Burns, Ronald M. Summers
In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.