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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Sun, Siqi Ji, Wei Tang, Liangfeng Zhang, Xiaobo Zhang, Yuejie Feng, Rui Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Fudan University Academy for Engineering and Technology Shanghai China Fudan University Children's Hospital Shanghai China

ISBN: (纸本)9781665468190

In this paper, we focus on the Bronchopulmonary Dysplasia (BPD) prediction task, which aims to identify the BPD in premature infants based on the given medical images. The existing methods for this task sometimes learn the spurious relations (confounders) between the image and label while ignore the causal features due to the limited size of the dataset, which largely influences the interpretability and robustness of the model. To address this challenge problem, we propose an interpretable causal method for BPD prediction, which can eliminate the irrelevant features and capture the causal features. We term our method as Causal Intervention by semantic instrumental Variable (CisiV). First, we design a causal structure graph modeling module to learn the representations of instrumental variables and confounders automatically. The constraints of confounders further guarantee the instrumental variables validity. Meanwhile, we add congenital attributes of premature infants to enable the learned instrumental variables to have medical semantics via a semantic matching module. We apply our model to BPD prediction in premature infants and achieve promising results. Extensive experiments indicate that CisiV could facilitate early intervention and provide support for clinical decision-making. Codes will be released to the community. © 2022 IEEE.

关键词： Semantics

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HarmoF0: Logarithmic Scale Dilated Convolution for Pitch Estimation

HarmoF0: Logarithmic Scale Dilated Convolution for Pitch Est...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wei, Weixing Li, Peilin Yu, Yi Li, Wei School of Computer Science and Technology Fudan University Shanghai China Digital Content and Media Sciences Research Division Tokyo Japan Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Sounds, especially music, contain various harmonic components scattered in the frequency dimension. It is difficult for normal convolutional neural networks to ob-serve these overtones. This paper introduces a multiple rates dilated causal convolution (MRDC-Conv) method to capture the harmonic structure in logarithmic scale spectrograms efficiently. The harmonic is helpful for pitch estimation, which is important for many sound processing applications. We propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and other dilated convolutions in pitch estimation. The re-sults show that this model outperforms the DeepF0, yields state-of-the-art performance in three datasets, and simultaneously reduces more than 90% parameters. We also find that it has stronger noise resistance and fewer octave errors. © 2022 IEEE.

关键词： Convolution

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Multimodal Music Emotion Recognition with Hierarchical Cross-Modal Attention Network

Multimodal Music Emotion Recognition with Hierarchical Cross...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhao, Jiahao Ru, Ganghui Yu, Yi Wu, Yulun Li, Dichucheng Li, Wei School of Computer Science and Technology Fudan University Shanghai China Digital Content and Media Sciences Research Division Tokyo Japan Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Computational music emotion recognition is to recognize the emotional content in music tracks. In computational music emotion recognition studies, researchers have paid close attention to the audio content of the music tracks. Although lyrics content and music context contribute greatly to the perceived emotion, these kinds of emotional information are usually ignored. Based on this finding, we propose a multimodal music emotion recognition method jointly predicting the valence and arousal values by combining the audio, lyrics, track name, and artist of a given track. Audio features, lyrics features and context features are extracted separately and fused by a cross-modal attention mechanism, forming a hierarchical structure. Our proposed model outperforms two baselines by a large margin and achieves state-of-the-art performance on two public datasets. © 2022 IEEE.

关键词： Emotion Recognition

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Evidential Open Set Recognition for Imbalanced Medical Images via Multi-level Data Augmentation

Evidential Open Set Recognition for Imbalanced Medical Image...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui-Wei Zhao Yiqian Xu Ying Zhang Rui Feng Academy for Engineering and Technology Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University College of Information Engineering Shanghai Maritime University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Due to the exstence of common and rare diseases, the complex clinical scenario often poses the challenging class imbalanced open set recognition problem. Unfortunately, most existing approaches are ill-suited for such situations with limited and imbalanced data during training and the possibility of encountering unseen classes during test. In this work, we propose a novel Multi-level mixup-based Evidential Open Set Recognition (ME-OSR) approach to more explicitly and effectively address the open set recognition for class-imbalanced medical images. Briefly, we first extract disentangled discriminative and background image features. Then, based on the original images and extracted features, we propose to sample and conduct Multi-level Open Mixup (MOM) for a more balanced open set data augmentation. It includes extensive intra- and interclass mixup operations in both image and feature spaces, which can augment rare classes with different feature combinations and generate potential pseudo-unknown class examples in the open set to boost the model training. Based on the augmented data and their extracted discriminative features, we propose a Regularized Evidential Deep Learning (REDL) classifier to work with the augmented data to achieve open set recognition with prediction uncertainty estimation and unknown example rejection. Through comparative experiments and ablation studies on several representative medical datasets, we showed that our proposed method outperforms other state-of-the-arts on four popular medical OSR datasets.

关键词： Training Deep learning Image recognition Uncertainty Image analysis Feature extraction Data augmentation Data mining Method of moments Biomedical imaging

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Prompting Large Language Models to Reformulate Queries for Moment Localization

arXiv

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arXiv 2023年

作者： Yan, Wenfeng Chen, Shaoxiang Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Meituan China

The task of moment localization is to localize a temporal moment in an untrimmed video for a given natural language query. Since untrimmed video contains highly redundant contents, the quality of the query is crucial for accurately localizing moments, i.e., the query should provide precise information about the target moment so that the localization model can understand what to look for in the videos. However, the natural language queries in current datasets may not be easy to understand for existing models. For example, the Ego4D dataset uses question sentences as the query to describe relatively complex moments. While being natural and straightforward for humans, understanding such question sentences are challenging for mainstream moment localization models like 2D-TAN. Inspired by the recent success of large language models, especially their ability of understanding and generating complex natural language contents, in this extended abstract, we make early attempts at reformulating the moment queries into a set of instructions using large language models and making them more friendly to the localization models. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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SIEDOB: Semantic Image Editing by Disentangling Object and Background

SIEDOB: Semantic Image Editing by Disentangling Object and B...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wuyang Luo Su Yang Xinjian Zhang Weishan Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University School of Computer Science and Technology China University of Petroleum

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. Consequently, they remain limited in processing content-rich images and suffer from generating unrealistic objects and texture-inconsistent backgrounds. To address this issue, we propose a novel paradigm, Semantic Image Editing by Disentangling Object and Background (SIEDOB), the core idea of which is to explicitly leverages several heterogeneous subnetworks for objects and backgrounds. First, SIEDOB disassembles the edited input into background regions and instance-level objects. Then, we feed them into the dedicated generators. Finally, all synthesized parts are embedded in their original locations and utilize a fusion network to obtain a harmonized result. Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB. We conduct extensive experiments on Cityscapes and ADE20K-Room datasets and exhibit that our method remarkably outperforms the baselines, especially in synthesizing realistic and diverse objects and texture-consistent backgrounds. Code is available at https://***/WuyangLuo/SIEDOB.

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SCSGNet: Spatial-Correlated and Shape-Guided Network for Breast Mass Segmentation

SCSGNet: Spatial-Correlated and Shape-Guided Network for Bre...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Qingqiu Li Jilan Xu Runtian Yuan Yuejie Zhang Rui Feng School of Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

Automatic and accurate breast mass segmentation plays a crucial role in the early diagnosis of breast cancer. However, it has been a challenging task for two main reasons: (1) Breast masses are diverse; and (2) The boundaries of masses are ambiguous. To address these problems, we propose a Spatial-Correlated and Shape-Guided Network (SCSGNet), which combines global context extraction with local boundary refinement. Specifically, the high-level features are aggregated to produce a global map as the initial guidance area, and a Series-Parallel Feature Fusion (SPFF) module is added to capture masses of different shapes and sizes. Besides, we design a Dynamic Long-range Correlation Capture (DLCC) module to capture the spatial correlation of masses at different positions. Finally, we devise a Triplet Attention Guide (TAG) module to iteratively update the feature map and refine the boundary. Experiments on two public datasets demonstrate that our method achieves superior performance over other state-of-the-art methods.

关键词： Correlation Shape Signal processing Feature extraction Breast cancer Acoustics Task analysis

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Downstream Task-agnostic Transferable Attacks on Language-Image Pre-training Models

Downstream Task-agnostic Transferable Attacks on Language-Im...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiqiang Lv Jingjing Chen Zhipeng Wei Kai Chen Zuxuan Wu Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing

Vision-language pre-trained models (e.g., CLIP) trained on large-scale datasets via self-supervised learning, are drawing increasing research attention since they can achieve superior performances on multi-modal downstream tasks. Nevertheless, we find that the adversarial perturbations crafted on vision-language pre-trained models can be used to attack different corresponding downstream task models. Specifically, to investigate such adversarial transferability, we introduce a task-agnostic method named Global and Local Augmentation (GLA) attack to generate highly transferable adversarial examples on CLIP, to attack black-box downstream task models. GLA adopts random crop and resize at both global and local patch levels, to create more diversity and make adversarial noises robust. Then GLA generates the adversarial perturbations by minimizing the cosine similarity between intermediate features from augmented adversarial and benign examples. Extensive experiments on three CLIP image encoders with different backbones and three different downstream tasks demonstrate the superiority of our method compared with other strong baselines. The code is available at https://***/yqlvcoding/GLAattack.

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Motion-Aware Video Paragraph Captioning via Exploring Object-Centered Internal Knowledge

Motion-Aware Video Paragraph Captioning via Exploring Object...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yimin Hu Guorui Yu Yuejie Zhang Rui Feng Tao Zhang Xuequan Lu Shang Gao School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University School of Information Management and Engineering Shanghai University of Finance and Economics School of Information Technology Deakin University

Video paragraph captioning task aims at generating a fine-grained, coherent and relevant paragraph for a video. Different from the images where objects are static, the temporal states of objects are changing in videos. The dynamic information could be contributed to understanding the whole video content. Existing works rarely put focus on modeling the dynamic changing state of the objects in the videos, causing the activities occurred in videos are poorly or wrongly depicted in paragraphs. To address this problem, we propose a novel Object State Tracking Network, which can capture the temporal state change of objects. However, due to the similarity of the consecutive frames in the videos, the information of the video is redundant and noisy. We further propose a semantic alignment mechanism, and enable the sentence information to refine the visual information. Extensive experiments on ActivityNet Captions demonstrate the effectiveness of our method.

关键词： Knowledge engineering Visualization Tracking Semantics Signal processing Acoustics Noise measurement

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