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Knowledge Abstraction Matching for Medical Question Answering

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Knowledge Abstraction Matching for Medical Question Answerin...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jun Chen Jingbo Zhou Zhenhui Shi Bin Fan Chengliang Luo Baidu Inc Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China

Medical Question Answering (medical QA), which studies the problem of automatically answering patients' medical questions online, is one of the major applications of bioinformatics. Though many efforts have been made before, the medical QA system still deserves delicate algorithm optimization due to the serious application scenario and strict requirement for the answer quality. In this paper, we introduce a novel Knowledge Abstraction Matching (KAM) method for the medical QA problem. The intuition of KAM is that there are many frequent repeat text segments appearing in the answers across different questions. From this view, we propose a new method that consists of frequent segment N-gram mining, medical knowledge abstraction, medical segment matching and answer re-retrieval. KAM has been incorporated into Baidu's enterprise medical QA system MelodyQA deployed on the backend of Muzhi Doctor. The evaluation shows that the proposed method can generate more quality answers for MelodyQA with a significant improvement of question coverage under acceptable accuracy.

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Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention

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arXiv 2021年

作者： Wu, Sitong Wu, Tianyi Tan, Haoru Guo, Guodong Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Recently, Transformers have shown promising performance in various vision tasks. To reduce the quadratic computation complexity caused by the global self-attention, various methods constrain the range of attention within a local region to improve its efficiency. Consequently, their receptive fields in a single attention layer are not large enough, resulting in insufficient context modeling. To address this issue, we propose a Pale-Shaped self-Attention (PS-Attention), which performs self-attention within a pale-shaped region. Compared to the global self-attention, PS-Attention can reduce the computation and memory costs significantly. Meanwhile, it can capture richer contextual information under the similar computation complexity with previous local self-attention mechanisms. Based on the PS-Attention, we develop a general Vision Transformer backbone with a hierarchical architecture, named Pale Transformer, which achieves 83.4%, 84.3%, and 84.9% Top-1 accuracy with the model size of 22M, 48M, and 85M respectively for 224 × 224 ImageNet-1K classification, outperforming the previous Vision Transformer backbones. For downstream tasks, our Pale Transformer backbone performs better than the recent state-of-the-art CSWin Transformer by a large margin on ADE20K semantic segmentation and COCO object detection & instance segmentation. The code will be released on https://***/BRIDL/PaddleViT. © 2021, CC BY.

关键词： Instance Segmentation

FCFR-Net: Feature fusion based coarse-to-fine residual learning for depth completion

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arXiv 2020年

作者： Liu, Lina Song, Xibin Lyu, Xiaoyang Diao, Junwei Wang, Mengmeng Liu, Yong Zhang, Liangjun Institute of Cyber-Systems and Control Zhejiang University China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China

Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate depth completion as a one-stage end-to-end learning task, which outputs dense depth maps directly. However, the feature extraction and supervision in one-stage frameworks are insufficient, limiting the performance of these approaches. To address this problem, we propose a novel end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i.e., a sparse-to-coarse stage and a coarse-to-fine stage. First, a coarse dense depth map is obtained by a simple CNN framework. Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with a coarse depth map and color image as input. Specially, in the coarse-to-fine stage, a channel shuffle extraction operation is utilized to extract more representative features from the color image and coarse depth map, and an energy based fusion operation is exploited to effectively fuse these features obtained by channel shuffle operation, thus leading to more accurate and refined depth maps. We achieve SoTA performance in RMSE on KITTI benchmark. Extensive experiments on other datasets future demonstrate the superiority of our approach over current state-of-the-art depth completion approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

Binarized neural architecture search for efficient object recognition

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arXiv 2020年

作者： Chen, Hanlin an Zhuo, Li Zhang, Baochang Zheng, Xiawu Liu, Jianzhuang Ji, Rongrong Doermann, David Guo, Guodong Beihang University Beijing China Xiamen University Fujian China Shenzhen Institutes of Advanced Technology University at Buffalo Institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application Shenzhen China

Traditional neural architecture search (NAS) has a significant impact in computer vision by automatically designing network architectures for various tasks. In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing. The BNAS calculation is more challenging than NAS due to the learning inefficiency caused by optimization requirements and the huge architecture space, and the performance loss when handling the wild data in various computing applications. To address these issues, we introduce operation space reduction and channel sampling into BNAS to significantly reduce the cost of searching. This is accomplished through a performance-based strategy that is robust to wild data, which is further used to abandon less potential operations. Furthermore, we introduce the Upper Confidence Bound (UCB) to solve 1-bit BNAS. Two optimization methods for binarized neural networks are used to validate the effectiveness of our BNAS. Extensive experiments demonstrate that the proposed BNAS achieves a comparable performance to NAS on both CIFAR and ImageNet databases. An accuracy of 96.53% vs. 97.22% is achieved on the CIFAR-10 dataset, but with a significantly compressed model, and a 40% faster search than the state-of-the-art PC-DARTS. Copyright © 2020, The Authors. All rights reserved.

关键词： Edge computing

Recurrent Bilinear Optimization for Binary Neural Networks

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arXiv 2022年

作者： Xu, Sheng Li, Yanjing Wang, Tiancheng Ma, Teli Zhang, Baochang Gao, Peng Qiao, Yu Lü, Jinhu Guo, Guodong Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Zhongguancun Laboratory Beijing China Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China

Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an essential role in reducing the performance gap to their real-valued counterparts. However, existing BNNs neglect the intrinsic bilinear relationship of real-valued weights and scale factors, resulting in a sub-optimal model caused by an insufficient training process. To address this issue, Recurrent Bilinear Optimization is proposed to improve the learning process of BNNs (RBONNs) by associating the intrinsic bilinear variables in the back propagation process. Our work is the first attempt to optimize BNNs from the bilinear perspective. Specifically, we employ a recurrent optimization and Density-ReLU to sequentially backtrack the sparse real-valued weight filters, which will be sufficiently trained and reach their performance limits based on a controllable learning process. We obtain robust RBONNs, which show impressive performance over state-of-the-art BNNs on various models and datasets. Particularly, on the task of object detection, RBONNs have great generalization performance. Our code is open-sourced on https://***/SteveTsui/RBONN. © 2022, CC BY.

关键词： Object detection

Interactive language acquisition with one-shot visual concept learning through a conversational game

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arXiv 2018年

作者： Zhang, Haichao Yu, Haonan Xu, Wei Baidu Research - Institue of Deep Learning Sunnyvale United States National Engineering Laboratory for Deep Learning Technology and Applications Beijing China

Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Intelligent agents

Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer

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arXiv 2022年

作者： Li, Yanjing Xu, Sheng Zhang, Baochang Cao, Xianbin Gao, Peng Guo, Guodong Beihang University Beijing China Zhongguancun Laboratory Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China

The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices. Among the powerful compression approaches, quantization extremely reduces the computation and memory consumption by low-bit parameters and bit-wise operations. However, low-bit ViTs remain largely unexplored and usually suffer from a significant performance drop compared with the real-valued counterparts. In this work, through extensive empirical analysis, we first identify the bottleneck for severe performance drop comes from the information distortion of the low-bit quantized self-attention map. We then develop an information rectification module (IRM) and a distribution guided distillation (DGD) scheme for fully quantized vision transformers (Q-ViT) to effectively eliminate such distortion, leading to a fully quantized ViTs. We evaluate our methods on popular DeiT and Swin backbones. Extensive experimental results show that our method achieves a much better performance than the prior arts. For example, our Q-ViT can theoretically accelerates the ViT-S by 6.14× and achieves about 80.9% Top-1 accuracy, even surpassing the full-precision counterpart by 1.0% on ImageNet dataset. Our codes and models are attached on https://***/YanjingLi0202/Q-ViT. © 2022, CC BY.

关键词： Vision

Fully transformer networks for semantic image segmentation

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arXiv 2021年

作者： Wu, Sitong Wu, Tianyi Lin, Fangjian Tian, Shengwei Guo, Guodong Institute of Deep Learning Baidu Research Beijing100085 China National Engineering Laboratory for Deep Learning Technology and Application Beijing100085 China Shengwei Tian are with School of Software XinJiang University Urumqi China

Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent progress has demonstrated that combining such Transformers with CNN-based semantic image segmentation models is very promising. However, it is not well studied yet on how well a pure Transformer based approach can achieve for image segmentation. In this work, we explore a novel framework for semantic image segmentation, which is encoder-decoder based Fully Transformer Networks (FTN). Specifically, we first propose a Pyramid Group Transformer (PGT) as the encoder for progressively learning hierarchical features, meanwhile reducing the computation complexity of the standard Visual Transformer (ViT). Then, we propose a Feature Pyramid Transformer (FPT) to fuse semantic-level and spatial-level information from multiple levels of the PGT encoder for semantic image segmentation. Surprisingly, this simple baseline can achieve better results on multiple challenging semantic segmentation and face parsing benchmarks, including PASCAL Context, ADE20K, COCOStuff, and CelebAMask-HQ. The source code will be released on https://***/BR-IDL/PaddleViT. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

A human-in-the-loop method for pulmonary nodule detection in CT scans

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an 710072 China Australian Institute for Machine Learning The University of Adelaide Adelaide 5000 SA Australia

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung cancer. Although extensive well-performed methods have been proposed for this task, they suffer from the domain shift issue between training and test images. Unsupervised domain adaptation (UDA) methods provide a promising means to mitigate the domain variance;however, their performance is still limited since no target domain supervision is introduced. To make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains, we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target domain. Specifically, we first train a detector model on source domain, and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme (USSS) to find a few target domain samples worth annotating most and obtain their human annotations. Finally, the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint (ZZC) strategy. We evaluate our method on the Nodule Analysis 2016 (LUNA16) and TianChi datasets. Experimental results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain. © The Author(s) 2024.

关键词： Domain adaptation Human-in-the-loop Pulmonary nodule detection Semi-supervised learning