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Discrepancy Matters: learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation

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arXiv 2023年

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Xia, Yong The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The Australian Institute for Machine Learning The University of Adelaide AdelaideSA5000 Australia

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that inconsistent decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancy obtained from two decoders, by feeding the discrepancy as a feedback signal to the encoder. The core design of LeFeD is to enlarge the difference by training differentiated decoders, and then learn from the inconsistent information iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles such as uncertainty estimation and strong constraints, as well as setting a new state-of-the-art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/LeFeD © 2023, CC BY.

关键词： Decoding

Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation

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arXiv 2021年

作者： Liao, Zehui Hu, Shishuai Xie, Yutong 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’an710072 China Australian Institute for Machine Learning The University of Adelaide AdelaideSA5000 Australia

Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators’ preferences. In this paper, we highlight the issue of annotator-related biases on medical image segmentation tasks, and propose a Preference-involved Annotation Distribution learning (PADL) framework to address it from the perspective of disentangling an annotator’s preference from stochastic errors using distribution learning so as to produce not only a meta segmentation but also the segmentation possibly made by each annotator. Under this framework, a stochastic error modeling (SEM) module estimates the meta segmentation map and average stochastic error map, and a series of human preference modeling (HPM) modules estimate each annotator’s segmentation and the corresponding stochastic error. We evaluated our PADL framework on two medical image benchmarks with different imaging modalities, which have been annotated by multiple medical professionals, and achieved promising performance on all five medical image segmentation tasks. © 2021, CC BY-NC-ND.

关键词： Stochastic systems

Intelligent exploration for user interface modules of mobile app with collective learning

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arXiv 2020年

作者： Zhou, Jingbo Tang, Zhenwei Zhao, Min Ge, Xiang Zhuang, Fuzhen Zhou, Meng Zou, Liming Yang, Chenglei Xiong, Hui Business Intelligence Lab Baidu Research Baidu TPG User Experience Department China National Engineering Laboratory of Deep Learning Technology and Application China Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Beijing University of Posts and Telecommunications China Peking University China Shandong University China Rutgers University United States

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is time-consuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China. Copyright © 2020, The Authors. All rights reserved.

关键词： User interfaces

Teaching Text-to-Image Models to Communicate in Dialog

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arXiv 2023年

作者： Sun, Xiaowen Feng, Jiazhan Wang, Yuxuan Lai, Yuxuan Shen, Xingyu Zhao, Dongyan Wangxuan Institute of Computer Technology Peking University China National Key Laboratory of General Artificial Intelligence BIGAI China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China Department of Computer Science The Open University of China China

A picture is worth a thousand words, thus, it is crucial for conversational agents to understand, perceive, and effectively respond with pictures. However, we find that directly employing conventional image generation techniques is inadequate for conversational agents to produce image responses effectively. In this paper, we focus on the innovative dialog-to-image generation task, where the model synthesizes a high-resolution image aligned with the given dialog context as a response. To tackle this problem, we design a tailored fine-tuning approach on the top of state-of-the-art text-to-image generation models to fully exploit the structural and semantic features in dialog context during image generation. Concretely, we linearize the dialog context with specific indicators to maintain the dialog structure, and employ in-domain data to alleviate the style mismatch between dialog-to-image and conventional image generation tasks. Empirical results on PhotoChat and MMDialog Corpus show that our approach brings consistent and remarkable improvement with 3 state-of-the-art pre-trained text-to-image generation backbones. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

The Speech Synthesis of Yi Language Based on DNN

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The Speech Synthesis of Yi Language Based on DNN

Information, Media and engineering (IJCIM), International Joint Conference on

作者： Xiaolong Bu Hongwu Yang Weizhao Zhang College of Physics and Electronic Engineering Northwest Normal University Lanzhou China School of Educational Technology National and provincial Joint Engineering Laboratory of Learning Analysis Technology in Online Education Northwest Normal University Lanzhou China College of Physics and Electronic Engineering Engineering Research Center of Gansu Province for Intelligent Information Technology and Application Northwest Normal University Lanzhou China

ISBN: (数字)9781728155869

ISBN: (纸本)9781728155876

This paper is mainly about a speech synthesis system based on deep Neural Network (DNN) model of Yi languages, a kind of minority language in China. The system is composed of relatively complete text analysis of Yi, model training and speech synthesis module. Especially in front-end, the word segmentation, pause handling, word-to-phoneme conversion and label processing are used to analysis text of Yi language. We designed the question set for decision tree of DNN model training and used vocoder: WORLD for synthesis. The system achieves a relatively good Mean Opinion Score (MOS) of 3.93 by Yi undergraduates as evaluators compared with a MOS of 4.58 of original speech. To investigate the factors affecting the quality of synthesized Yi speech, this paper also objectively evaluates the performance of different training set and DNN model. The system successfully synthesized Yi speech for the first time and synthesized speech is relatively good as the result of an only complete minority language speech synthesis system.

关键词： Speech synthesis Text analysis Training Analytical models Hidden Markov models Dictionaries

The Combined Effect of Konjac Glucomannan and Ultrasound Treatment on the Interaction of Surimi and Gluten Protein

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SSRN

SSRN 2024年

作者： Cao, Geng Yang, Zuoqian Li, Xiangzheng He, Xiaoyang Song, Shuang Wen, Chengrong National Engineering Research Center for Seafood State Key Laboratory of Marine Food Processing and Safety Control Collaborative Innovation Center of Seafood Deep Processing National Engineering Research Center of Seafood National & Local Joint Engineering Laboratory for Marine Bioactive Polysaccharide Development and Application School of Food Science and Technology Dalian Polytechnic University Dalian116034 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China

Gluten, surimi and mixted protein were analyzed for changes in protein structure after treatment with konjac glucomannan (KGM), ultrasound (U) and konjac glucomannan-ultrasound (UKGM). In this study, molecular weight, fluorescence intensity, intermolecular force, disulfide bond, microstructure, SDS-soluble protein and free amino acid content were investigated. All treatments increased, Imax, SH and free amino groups of gluten, while decreasing SS bonds. KGM and U treatment decreased the SDS-soluble protein, SH groups and Imax of surimi protein while increasing SS bonds. However, the effect of UKGM treatment was the opposite. For the mixed proteins, all treatments were found to decrease the Imax and SH groups, while increasing the SS group. KGM and UKGM treatments increased protein with ionic and hydrogen bond contents, while U treatment increased hydrophobic bonds. Consequently, UKGM treatment was able to improve the interaction of surimi and gluten and resulted in higher structural properties of the mixed protein. © 2024, The Authors. All rights reserved.

关键词： Proteins

QuantYOLO: A High-Throughput and Power-Efficient Object Detection Network for Resource and Power Constrained UAVs

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QuantYOLO: A High-Throughput and Power-Efficient Object Dete...

Proceedings of the Digital Image Computing: Technqiues and applications (DICTA)

作者： Muhammad Gohar Javed Minahil Raza Muhammad Mohsin Ghaffar Christian Weis Norbert Wehn Muhammad Shahzad Faisal Shafait School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan Microelectronic Systems Design Research Group Technische Universität Kaiserslautern Germany Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Pakistan Data Science in Earth Observation TU Munich Germany

Convolutional Neural Networks (CNNs) are producing state-of-the-art results in the object detection field. However, deep topologies of CNN are computationally intensive and typically require excessive resources (i.e. high-end GPUs), which hinder their deployment on resource and power constrained UAVs. In this work, we present a high-throughput and power efficient quantized object detection network, QuantYOLO, which is based on the Tiny-YOLOv2 topology. We conduct a detailed exploration of precision and filter pruning vs. accuracy, throughput and power consumption trade-off for the object detection task. As a result of these explorations, we select a network with binarized weights and 4-bit activations (except the output layer), which is 21.8× smaller than the Tiny-YOLOv2 achieving a mean Average Precision (mAP) of 51.5% on the PASCAL-VOC dataset. Finally, we present an FPGA based accelerator, which achieves 1.6× higher throughput (FPS) and is 3.1× more power efficient as compared to prior FPGA architectures.

关键词： Quantization (signal) Power demand Network topology Object detection Computer architecture Throughput Topology

Feature engineering Meets deep learning: A Case Study on Table Detection in Documents

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Feature Engineering Meets Deep Learning: A Case Study on Tab...

Proceedings of the Digital Image Computing: Technqiues and applications (DICTA)

作者： Muhammad Ali Shahzad Rabeya Noor Sheraz Ahmad Ajmal Mian Faisal Shafait School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany The University of Western Australia Perth Australia Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Islamabad Pakistan

Traditional computer vision approaches heavily relied on hand-crafted features for tasks such as visual object detection and recognition. The recent success of deep learning in automatically extracting representative and powerful features from images has brought a paradigm shift in this area. As a side effect, decades of research into hand-crafted features is considered outdated. In this paper, we present an approach for table detection in which we leverage a deep learning based table detection model with hand-crafted features from a classical table detection method. We demonstrate that by using a suitable encoding of hand-crafted features, the deep learning model is able to perform better at the detection task. Experiments on publicly available UNLV dataset show that the presented method achieves an accuracy comparable with the state-of-the-art deep learning methods without the need of extensive hyper-parameter tuning.

关键词： Feature extraction deep learning Image color analysis Object detection Task analysis Encoding Layout

Kham Dialect Speech Synthesis Based on deep learning

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Kham Dialect Speech Synthesis Based on Deep Learning

Information, Media and engineering (IJCIM), International Joint Conference on

作者： Weizhao Zhang Hongwu Yang Xiaolong Bu College of Physics and Electronic Engineering Engineering Research Center of Gansu Province for Intelligent Information Technology and Application Northwest Normal University Lanzhou China School of Educational Technology National and Provincial Joint Engineering Laboratory of Learning Analysis Technology in Online Education College of Physics and Electronic Engineering Northwest Normal University Lanzhou China College of Physics and Electronic Engineering Northwest Normal University Lanzhou China

ISBN: (数字)9781728155869

ISBN: (纸本)9781728155876

In this paper, we constructed speech synthesis corpus of Kham dialect. At the same time, we designed SAMP-Kham machine-readable phonetic label of Kham dialect, and proposed a framework of Kham dialect speech synthesis based on deep learning. The deep learning architecture includes deep neural network (DNN), hybrid long short-term memory (LSTM) and hybrid Bidirectional long short-term memory (BLSTM). The objective and subjective evaluations test showed that quality of synthesized speech of Kham dialect was satisfied.

关键词： Speech synthesis Machine learning Hidden Markov models Acoustics Adaptation models Training Neural networks