Search Results for author: Yi Lu

Found 27 papers, 4 papers with code

DePS: An improved deep learning model for de novo peptide sequencing

no code implementations16 Mar 2022 Cheng Ge, Yi Lu, Jia Qu, Liangxu Xie, Feng Wang, Hong Zhang, Ren Kong, Shan Chang

De novo peptide sequencing from mass spectrometry data is an important method for protein identification.

C+1 Loss: Learn to Classify C Classes of Interest and the Background Class Differentially

no code implementations29 Sep 2021 Changhuai Chen, Xile Shen, Mengyu Ye, Yi Lu, Jun Che, ShiLiang Pu

We figure out that the background class should be treated differently from the classes of interest during training.

Classification Human Parsing +2

Joint Positioning and Tracking via NR Sidelink in 5G-Empowered Industrial IoT: Releasing the Potential of V2X Technology

no code implementations15 Jan 2021 Yi Lu, Mike Koivisto, Jukka Talvitie, Elizaveta Rastorgueva-Foi, Toni Levanen, Elena Simona Lohan, Mikko Valkama

The fifth generation (5G) mobile networks with enhanced connectivity and positioning capabilities play an increasingly important role in the development of automated vehicle-to-everything (V2X) and other advanced industrial Internet of Things (IoT) systems.

On the Transferability of Minimal Prediction Preserving Inputs in Question Answering

no code implementations NAACL 2021 Shayne Longpre, Yi Lu, Christopher DuBois

In the context of question answering, we investigate competing hypotheses for the existence of MPPIs, including poor posterior calibration of neural models, lack of pretraining, and "dataset bias" (where a model learns to attend to spurious, non-generalizable cues in the training data).

Adversarial Robustness Question Answering

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

1 code implementation Radiology 2020 Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia

Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.

COVID-19 Image Segmentation

Graph-FCN for image semantic segmentation

no code implementations2 Jan 2020 Yi Lu, Yaran Chen, Dongbin Zhao, Jianxin Chen

Then we apply graph convolutional network to solve this graph node classification problem.

General Classification Node Classification +1

An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering

no code implementations WS 2019 Shayne Longpre, Yi Lu, Zhucheng Tu, Chris DuBois

To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation.

Data Augmentation Question Answering +2

DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction

no code implementations25 Mar 2019 Zhihui Guo, Junjie Bai, Yi Lu, Xin Wang, Kunlin Cao, Qi Song, Milan Sonka, Youbing Yin

The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask.

Semantic Segmentation

Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding

no code implementations29 Jan 2019 Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Kunlin Cao, Qi Song, Shaoting Zhang, Siwei Lyu, Youbing Yin

In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.

Residual Attention based Network for Hand Bone Age Assessment

no code implementations21 Dec 2018 Eric Wu, Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Shaoting Zhang, Kunlin Cao, Qi Song, Siwei Lyu, Youbing Yin

The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.

Hand Segmentation

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