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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

IEEE/ACM Transactions on computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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A hybrid FEM-PINN method for time-dependent partial differential equations

arXiv

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arXiv 2024年

作者： Feng, Xiaodong Shangguan, Haojiong Tang, Tao Wan, Xiaoliang Zhou, Tao Division of Science and Technology BNU-HKBU United International College Zhuhai519087 China School of Electrical and Computer Engineering Guangzhou Nanfang College Guangzhou510970 China Department of Mathematics Center for Computation and Technology Louisiana State University Baton Rouge70803 United States Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we present a hybrid numerical method for solving evolution partial differential equations (PDEs) by merging the time finite element method with deep neural networks. In contrast to the conventional deep learning-based formulation where the neural network is defined on a spatiotemporal domain, our methodology utilizes finite element basis functions in the time direction where the space-dependent coefficients are defined as the output of a neural network. We then apply the Galerkin or collocation projection in the time direction to obtain a system of PDEs for the space-dependent coefficients which is approximated in the framework of PINN. The advantages of such a hybrid formulation are twofold: statistical errors are avoided for the integral in the time direction, and the neural network’s output can be regarded as a set of reduced spatial basis functions. To further alleviate the difficulties from high dimensionality and low regularity, we have developed an adaptive sampling strategy that refines the training set. More specifically, we use an explicit density model to approximate the distribution induced by the PDE residual and then augment the training set with new time-dependent random samples given by the learned density model. The effectiveness and efficiency of our proposed method have been demonstrated through a series of numerical experiments. © 2024, CC BY.

关键词： Partial differential equations

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MULTI-LEVEL PROTEIN STRUCTURE PRE-TRAINING WITH PROMPT LEARNING 11

MULTI-LEVEL PROTEIN STRUCTURE PRE-TRAINING WITH PROMPT LEARN...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Zeyuan Zhang, Qiang Yu, Haoran Hu, Shuangwei Jin, Xurui Gong, Zhichen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China College of Chemical and Biological Engineering Zhejiang University China Vecx Biomedicines Inc. MindRank AI Ltd. University College London United Kingdom AZFT Joint Lab for Knowledge Engine China East China Sea Laboratory China

A protein can focus on different structure levels to implement its functions. Each structure has its own merit and driving forces in describing specific characteristics, and they cannot replace each other. Most existing function prediction methods take either the primary or the tertiary structure as input, unintentionally ignoring the other levels of protein structures. Considering protein sequences can determine multi-level structures, in this paper, we aim to realize the comprehensive potential of protein sequences for function prediction. Specifically, we propose a new prompt-guided multi-task pre-training and fine-tuning framework. Through the prompt-guided multi-task pre-training, we learn multiple prompt signals to steer the model, called PromptProtein, to focus on different levels of structures. We also design a prompt fine-tuning module to provide downstream tasks the on-demand flexibility of utilizing respective levels of structural information. Extensive experiments on function prediction and protein engineering show that PromptProtein outperforms state-of-the-art methods by large margins. To the best of our knowledge, this is the first prompt-based pre-trained protein model. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Proteins

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Short Tail:taming tail latency for erasure-code-based in-memory systems

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Frontiers of Information Technology & Electronic engineering 2022年第11期23卷 1646-1657页

作者： Yun TENG Zhiyue LI Jing HUANG Guangyan ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China Beijing National Research Center for Information Science and Technology(Tsinghua University) Beijing 100084China

In-memory systems with erasure coding(EC)enabled are widely used to achieve high performance and data ***,as the scale of clusters grows,the server-level fail-slow problem is becoming increasingly frequent,which can create long tail *** influence of long tail latency is further amplified in EC-based systems due to the synchronous nature of multiple EC *** this paper,we propose an EC-enabled in-memory storage system called ShortTail,which can achieve consistent performance and low latency for both reads and ***,ShortTail uses a lightweight request monitor to track the performance of each memory node and identify any fail-slow ***,ShortTail selectively performs degraded reads and redirected writes to avoid accessing fail-slow ***,ShortTail posts an adaptive write strategy to reduce write amplification of small *** implement ShortTail on top of Memcached and compare it with two baseline *** experimental results show that ShortTail can reduce the P99 tail latency by up to 63.77%;it also brings significant improvements in the median latency and average latency.

关键词： Erasure code In-memory system Node fail-slow Small write Tail latency

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A Study and Recognition of Alzheimer's Disease Using Machine Learning 8

A Study and Recognition of Alzheimer's Disease Using Machine...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Basha, S.K. Masthan Ashalatha, G. Vara Prasad, Srisailapu D Al-Hameed, Mazin R. Zearah, Sajad Ali Saikumar, K. Vignan's Institute of Management and Technology for Women ECE Department Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science-CS-DS&AI&DS Telangana Hyderabad India GITAM Deemed to Be University Computer Science and Engineering School of Technology Telangana Hyderabad India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798350334012

Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how concerned they are about it. The way we live today is different from how it was in the past because of our working environment and the lack of opportunities to interact with people. WHO estimates that about 10 billion individuals perish each year due to Alzheimer's disease since not been appropriately diagnosed and treated. An advanced Machine Learning-based recognition system design performs in this research work. The proposed model has been designed based on the human being EEG dataset(testing) and the Kaggle Alzheimer dataset (training). The developed intelligent system will be updated with data collection and pre-processing through the Random Forest machine model to locate our health condition and recognize daily routine changes. Only by developing a machine-based approach will we be able to discover Alzheimer's patients who previously had no idea that they had the illness and are now unable to reverse the progression of their condition. The designed application has attained a sensitivity of 93.01%, an accuracy of 91.32%, a recall of 97.28%, and a detection rate of 98.91%. The implemented application is beneficial to investigators and surgeons for easy treatment. The proposed RFO Machine learning design competes with present technology and outperforms methodology. © 2023 IEEE.

关键词： Intelligent systems

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A Formal Methodology for Verifying Side-Channel Vulnerabilities in Cache Architectures 1

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23rd International Conference on Formal engineering Methods, ICFEM 2022

作者： Jiang, Ke Zhang, Tianwei Sanán, David Zhao, Yongwang Liu, Yang School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Information and Communication Technologies Singapore Institute of Technology Singapore Singapore ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9783031172441

ISBN: (纸本)9783031172434

Security-aware CPU caches have been designed to mitigate side-channel attacks and prevent information leakage. How to validate the effectiveness of these designs remains an unsolved problem. Prior works assess the security of architectures empirically without a formal guarantee, making the evaluation results less convincing. In this paper, we propose a comprehensive methodology based on formal methods for security verification of cache architectures. Specifically, we design an entropy-based noninterference reasoning framework with two unwinding conditions to assess the information leakage of the cache designs. The reasoning framework quantifies the dependency relationships by the mutual information between the distributions of input and output of side channels. Given a cache design, we formalize its behavior specification along with the cache layouts into an abstract state machine, to instantiate the parameterized reasoning framework that discloses any potential vulnerabilities. We use our methodology to assess eight state-of-the-art cache architectures to demonstrate reliability as well as flexibility. © 2022, Springer Nature Switzerland AG.

关键词： Side channel attack

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Dataset-Level Color Augmentation and Multi-Scale Exploration Methods for Polyp Segmentation

SSRN

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SSRN 2024年

作者： Chen, Haipeng Ju, Honghong Qin, Jun Song, Jincai Lyu, Yingda Liu, Xianzhu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Public Computer Education and Research Center Jilin University Changchun130012 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China College of Opto-Electronic Engineering Changchun University of Science and Technology Changchun130022 China

Automatic segmentation of polyps from colonoscopy images plays a critical role in early screening and treatment of colorectal cancer. Although deep learning methods have made significant progress, precise polyp segmentation faces two challenges: (1) the imbalance of color appearances in the limited training dataset hinders the generalization of the model, and (2) polyps have the diverse scales, locations and shapes with blurred boundary. To address the issues, Dataset-Level Color Augmentation (DLCA) and Convolutional Multi-scale Attention Module (CMAM) are proposed. DLCA employs the dataset-level color knowledge and generate new color appearances, to avoid the model learning false associations with colors. Meanwhile, CMAM simultaneously explores the region and boundary clues and model multi-scale context, which improves the accuracy of polyp localization and fine-grained segmentation. We conduct comprehensive experiments and compare our network with state-of-the-art methods. The proposed model is superior on multiple polyp datasets, especially on ETIS, where mDice and mIoU reach 0.839 and 0.766. Furthermore, it's validated that DLCA can be widely applied to most polyp segmentation methods, and CMAM is practical and plug-and-play. Finally, the model demonstrates promising generalization to breast ultrasound and skin lesion segmentation. © 2024, The Authors. All rights reserved.

关键词： Color

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Detection of GAN Generated Fake Satellite Images Using Deep Learning 3

Detection of GAN Generated Fake Satellite Images Using Deep ...

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3rd International Conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2023

作者： Snehith, K. Anuradha, G. Vemuri Krishna N, Hari Sathishkumar, V.E. Abualigah, Laith Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Industrial Engineering Hanyang University 222 Wangsimini-ro Seongdong-gu Seoul Korea Republic of Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Meu Research Unit Middle East University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9781643684581

Fake Images generation has become a common practice nowadays and this process has become elementary with the advent of Generative Adversarial Networks. Although though fake images are entertaining and have their benefits, they also have a few negative effects. Modern satellite images offer crucial data that aids in tracking various packages, including spatial decision, spectral properties, sensor sensitivity, and many others. Critical details needed for a few applications may be massively obtained from these photos. Yet, altering these images by removing, incorporating, or replicating objects leads to false perceptions of reality. It is our intention to come across those kind of fake satellite images. We use in-depth learning architectures to identify fake Satellite photos in order to distinguish between authentic and fake photos. © 2023 The Authors.

关键词： Generative adversarial networks

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A Block-Coordinate-Descent Robust Approach to Incentive-Based Integrated Demand Response in Managing Multienergy Hubs with Must-Run Processes

A Block-Coordinate-Descent Robust Approach to Incentive-Base...

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作者： Aghamohamadi, Mehrdad Mahmoudi, Amin Ward, John K. Haque, Mohammed H. Catalao, Joao P. S. College of Science and Engineering Flinders University AdelaideSA Australia Scientific and Industrial Research Organization CSIRO Energy Center Mayfield WestNSW Australia UniSA STEM University of South Australia AdelaideSA Australia Institute for Systems and Computer Engineering Technology and Science Faculty of Engineering University of Porto Porto Portugal

This article presents a new robust incentive-based integrated demand response (IDR) model for energy hub systems (EHSs). The considered incentive-based demand response (IBDR) schemes are interruptible/curtailable service and capacity market program. The proposed IDR model integrates the arbitrage ability of EHS storages as well as energy conversion into the IDR model. The objective of the IDR optimization problem is to maximize/minimize the allocated incentives/penalties in targeted time periods by IBDR schemes while supplying must-run processes with no interruption. Uncertainties of load and energy prices are considered through user-defined polyhedral uncertainty sets. A trilevel robust optimization (RO) is developed, which includes a trilevel min-max-min problem. To solve the trilevel adaptive robust model, the column-and-constraint generation technique is employed by means of a decomposition methodology recasting the trilevel model into a single-level min problem and a bilevel max-min problem. Unlike previous RO models that solve the inner max-min problem by duality theory, a block-coordinate-descent (BCD) methodology is used to solve the max-min problem by means of the first-order Taylor series in this study. The use of the BCD technique instead of duality theory enables a recourse-based characterization of integer variables, such as EHS storage status, which was not applicable in previous models (due to the use of duality theory). Moreover, Lagrange multipliers are eliminated as no duality is conducted. A postevent analysis is conducted to justify the long-term performance of the robust solutions and determine the optimal settings of the BCD robust approach. Results indicate that the IDR model significantly reduces the EHS input electricity in targeted time periods (four hours per day) by IBDR schemes and covers the required electricity with must-run processes by combined heat and power unit, using natural gas. This implies a 2.13% reduction in the operation co

关键词： Lagrange multipliers

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Managing prosumer exports in distribution networks through participatory export limits

Managing prosumer exports in distribution networks through p...

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CIRED 2024 Vienna Workshop

作者： Ashish Kumar Karmaker Sam Behrens Bjorn Sturmberg Hemanshu Pota School of Engineering and Technology University of New South Wales Canberra Australia Energy Center Commonwealth Scientific and Industrial Research Organization Newcastle Australia College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (数字)9781837241224

This paper proposes an innovative stakeholder-centric approach for managing prosumer exports in distribution networks ensuring stakeholder benefits. The rapid inclusion of photovoltaic (PV) systems leads to excessive exports, affecting the stability and performance of the distribution networks and fostering effective export management. In addition, the emergence of vehicle-to-grid (V2G) services presents challenges that may exacerbate export-related issues. Several distribution companies use fixed export limits for PV prosumers without looking at network performance. Current studies suggest dynamic limits to unlock export capabilities, providing variable export opportunities while neglecting diverse capabilities and nodal disparities of prosumers. By amalgamating dynamic export limits and prosumer priority indexing, this paper endeavours to derive individualized export limits in distribution networks. This research examined dynamic export capabilities on a real Australian suburban distribution feeder with 966 customers utilizing diverse capacities, nodal situations, and network uncertainties. The prosumer priority ranking is based on nodal constraints, time, and individual capabilities. The paper highlights participatory export limits, utilizing dynamic export limits and individual rankings, to offer enhanced export flexibility, benefiting stakeholders both distribution network operators and prosumers. These stakeholder-centric export limits provide a promising avenue for seamless PV and V2G integration toward a sustainable and resilient energy ecosystem.

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