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Enhancing skin cancer detection through category representation and fusion of pre-trained models

Information Fusion 2025年

作者： Lingping Kong Juan D. Velasquez Václav Snášel Millie Pant Jeng-Shyang Pan Jana Nowakova Department of Computer Science VSB-Technical University of Ostrava listpodu 17 Czech Republic Department of Industrial Engineering University of Chile Santiago O’Higgins 1058 Chile Instituto Sistemas Complejos de Ingeniería Santiago Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand 247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand 247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology 219 Ningliu Road China College of Computer Science and Engineering Shandong University of Science and Technology China

The use of pre-trained models in medical image classification has gained significant attention due to their ability to handle complex datasets and improve accuracy. However, challenges such as domain-specific customization, interpretability, and computational efficiency remain critical, especially in high-stakes applications such as skin cancer detection. In this paper, we introduce a novel interpretability-assisted fine-tuning framework that leverages category representation to enhance both model accuracy and transparency. Using the widely known HAM10000 dataset, which includes seven imbalanced categories of skin cancer, we demonstrate that our method improves the classification accuracy by 2.6% compared to standard pre-trained models. In addition to precision, we also achieve significant improvements in interpretability, with our category representation framework providing more understandable insights into the model’s decision-making process. Key metrics, such as precision and recall, show enhanced performance, particularly for underrepresented skin cancer types such as Melanocytic Nevi (F1 score of 0.94) and Actinic Keratosis (F1 score of 0.66). Furthermore, the prediction accuracy of the proposed model of the top-3 reaches 98. 21%, which is highly significant for medical decision making. This observation in interpredibility underscores the value of top- n predictions, especially in challenging cases, to support more informed and accurate decisions. The proposed fusion framework not only enhances predictive accuracy, but also offers an interpretable model output that can assist clinicians in making informed decisions. This makes our approach particularly relevant in medical applications, where both accuracy and transparency are crucial. Our results highlight the potential of fusing pretrained models with category representation techniques to bridge the gap between performance and interpretability in AI-driven healthcare solutions.

关键词： Pre-trained model Sparse representation Skin cancer data Model interpredibility

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Efficient Numerical Computation of Time-Fractional Nonlinear Schrodinger Equations in Unbounded Domain

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Communications in computational Physics 2019年第1期25卷 218-243页

作者： Jiwei Zhang Dongfang Li Xavier Antoine Beijing Computational Science Research Center Beijing 100193P.R.China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China Institut Elie Cartan de Lorraine UMR CNRS 7502Universit́e de LorraineInria Nancy-Grand EstSPHINX TeamF-54506 Vandoeuvre-l`es-Nancy CedexFrance

The aim of this paper is to derive a stable and efficient scheme for solving the one-dimensional time-fractional nonlinear Schrodinger equation set in an unbounded *** first derive absorbing boundary conditions for the fractional system by using the unified approach introduced in[47,48]and a linearization ***,the initial boundary-value problem for the fractional system with ABCs is discretized,a stability analysis is developed and the error estimate O(h^(2)+τ)is *** accel-erate the L1-scheme in time,a sum-of-exponentials approximation is introduced to speed-up the evaluation of the Caputo fractional *** resulting algorithm is highly efficient for long time ***,we end the paper by reporting some numerical simulations to validate the properties(accuracy and efficiency)of the derived scheme.

关键词： Time-fractional nonlinear Schrodinger equation absorbing boundary condition sta-bility analysis convergence analysis sum-of-exponentials approximation

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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Ball $k$k-Means: Fast Adaptive Clustering With No Bounds

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2020年第1期44卷 87-99页

作者： Shuyin Xia Daowan Peng Deyu Meng Changqing Zhang Guoyin Wang Elisabeth Giem Wei Wei Zizhong Chen Department of Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa Macau China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Engineering University of California Riverside Riverside CA USA School of Computer Science and Engineering Xi'an University of Technology Xi'an China

This paper presents a novel accelerated exact $k$ -means called as “Ball $k$ -means” by using the ball to describe each cluster, which focus on reducing the point-centroid distance computation. The “Ball $k$ -means” can exactly find its neighbor clusters for each cluster, resulting distance computations only between a point and its neighbor clusters’ centroids instead of all centroids. What’s more, each cluster can be divided into “stable area” and “active area”, and the latter one is further divided into some exact “annular area”. The assignment of the points in the “stable area” is not changed while the points in each “annular area” will be adjusted within a few neighbor clusters. There are no upper or lower bounds in the whole process. Moreover, ball $k$ -means uses ball clusters and neighbor searching along with multiple novel stratagems for reducing centroid distance computations. In comparison with the current state-of-the art accelerated exact bounded methods, the Yinyang algorithm and the Exponion algorithm, as well as other top-of-the-line tree-based and bounded methods, the ball $k$ -means attains both higher performance and performs fewer distance calculations, especially for large-k problems. The faster speed, no extra parameters and simpler design of “Ball $k$ -means” make it an all-around replacement of the naive $k$ -means.

关键词： Clustering algorithms Approximation algorithms Acceleration Partitioning algorithms Standards Laboratories Time complexity

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Upscaled Model for Mixed Dimensional Coupled Flow Problem in Fractured Porous Media Using Non-local Multicontinuum (NLMC) Method 7th

Upscaled Model for Mixed Dimensional Coupled Flow Problem in...

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7th International Conference on Finite Difference Methods, FDM 2018

作者： Vasilyeva, Maria Chung, Eric T. Efendiev, Yalchin Leung, Wing Tat Wang, Yating Institute for Scientific Computation Texas A&M University College StationTX United States North-Eastern Federal University Yakutsk Russia Shatin Hong Kong Department of Mathematics Texas A&M University College StationTX United States Institute of Computational Engineering and Sciences University of Texas at Austin Austin United States

ISBN: (纸本)9783030115388

In this paper, we consider a mixed dimensional discrete fracture model with highly conductive fractures. Mathematically the problem is described by a coupled system of equations consisting a d - dimensional equation for flow in porous matrix and a (d-1) - dimensional equation for fracture networks with a specific exchange term for coupling them. For the numerical solution on the fine grid, we construct unstructured mesh that is conforming with fracture surface and use the finite element approximation. Fine grid approximation typically leads to very large systems of equations since it resolves the fracture networks, and therefore some multiscale methods or upscaling methods should be applied. The main contribution of this paper is that we propose a new upscaled model using Non-local multi-continuum (NLMC) method and construct an effective coarse grid approximation. The upscaled model has only one additional coarse degree of freedom (DOF) for each fracture network. We will present results of the numerical simulations using our proposed upscaling method to illustrate its performance. © 2019, Springer Nature Switzerland AG.

关键词： Fracture

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Rubric-based holistic review represents a change from traditional graduate admissions approaches in physics

arXiv

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arXiv 2021年

作者： Young, Nicholas T. Verboncoeur, N. Lam, Dao Chi Caballero, Marcos D. Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Statistics Michigan State University East LansingMI48824 United States Center for Computing in Science Education Department of Physics University of Oslo OsloN-0316 Norway CREATE for STEM Institute Michigan State University East LansingMI48824 United States

Rubric-based admissions are claimed to help make the graduate admissions process more equitable, possibly helping to address the historical and ongoing inequities in the U.S. physics graduate school admissions process that have often excluded applicants from minoritized races, ethnicities, genders, and backgrounds. Yet, no studies have examined whether rubric-based admissions methods represent a fundamental change of the admissions process or simply represent a new tool that achieves the same outcome. To address that, we developed supervised machine learning models of graduate admissions data collected from our department over a seven-year period. During the first four years, our department used a traditional admissions process and switched to a rubric-based process for the following three years, allowing us to compare which parts of the applications were used to drive admissions decisions. We find that faculty focused on applicants' physics GRE scores and grade point averages when making admissions decisions before the implementation of the rubric. While we were able to develop a sufficiently good model whose results we could trust for the data before the implementation of the rubric, we were unable to do so for the data collected after the implementation of the rubric, despite multiple modifications to the algorithms and data such as implementing Tomek Links. Our inability to model the second data set despite being able to model the first combined with model comparison analyses suggests that rubric-based admissions does change the underlying process. These results suggest that rubric-based holistic review is a method that could make the graduate admissions process in physics more equitable. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Automated incisional hernia characterization by non-rigid registration of CT images-A pilot study

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Current Directions in Biomedical engineering 2020年第3期6卷

作者： Voß, Samuel Lösel, Philipp D. Heuveline, Vincent Saalfeld, Sylvia Berg, Philipp Kallinowski, Friedrich Forschungscampus STIMULATE Department of Fluid Dynamics and Technical Flows University of Magdeburg Universitaetsplatz 2 Magdeburg 39106 Germany Heidelberg Institute for Theoretical Studies Heidelberg Germany Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg Germany Forschungscampus STIMULATE University of Magdeburg Magdeburg Germany General Visceral and Transplantation Surgery University Hospital Heidelberg Heidelberg Germany General and Visceral Surgery GRN Hospital Eberbach Eberbach Germany

Incisional hernia repair makes use of prosthetic meshes to re-establish a biomechanically stable abdominal wall. Mesh sizing and fixation have been found to be essential for the clinical outcome. Comparative CT images a) under rest versus b) under Valsalva maneuver (exhalation against closed airways) provide useful information for hernia characterization. However, this process incorporates several manual measurements, which led to observer variability. The present study suggests using an image registration approach of the CT data to reliably and reproducibly extract hernia quantities. The routine is implemented in the software framework MATLAB and works fully automatic. After CT data import, slice by slice undergo non-rigid B-spline grid registration. Local displacement and strain are extracted from the transformation field. The qualitative results correspond to the clinical observation. Maximum displacement of 3.5 cm and maximum strain of 25 % are calculated for one patient's data set. Current approaches do not provide this type of information. Further research will focus on validation and possibilities to include this new kind of knowledge into the design process of prosthetic meshes. © 2020 by Walter de Gruyter Berlin/Boston 2020.

关键词： Prosthetics

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A note on the ill-posedness of shear flow for the MHD boundary layer equations

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Science China mathematics 2018年第11期61卷 2065-2078页

作者： Cheng-Jie Liu Feng Xie Tong Yang Institute of Natural Sciences Shanghai Jiao Tong University School of Mathematical Sciences and Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong University Department of Mathematics City University of Hong Kong

For the two-dimensional Magnetohydrodynamics(MHD)boundary layer system,it has been shown that the non-degenerate tangential magnetic field leads to the well-posedness in Sobolev spaces and high Reynolds number limits without any monotonicity condition on the velocity field in our previous *** paper aims to show that sufficient degeneracy in the tangential magnetic field at a non-degenerate critical point of the tangential velocity field of shear flow indeed yields instability as for the classical Prandtl equations without magnetic field studied by G′erard-Varet and Dormy(2010).This partially shows the necessity of the non-degeneracy in the tangential magnetic field for the stability of the boundary layer of MHD in 2D at least in Sobolev spaces.

关键词： MHD boundary layer Prandtl equations shear flows ill-posedness Sobolev spaces

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Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory

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Physical Review D 2024年第9期109卷 094512-094512页

作者： Ed Bennett Deog Ki Hong Ho Hsiao Jong-Wan Lee C.-J. David Lin Biagio Lucini Maurizio Piai Davide Vadacchino Swansea Academy of Advanced Computing Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Pusan National University Busan 46241 Korea Extreme Physics Institute Pusan National University Busan 46241 Korea Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Particle Theory and Cosmology Group Center for Theoretical Physics of the Universe Institute for Basic Science (IBS) Daejeon 34126 Korea Center for High Energy Physics Chung-Yuan Christian University Chung-Li 32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SA2 8PP Swansea Wales United Kingdom Centre for Mathematical Sciences University of Plymouth Plymouth PL4 8AA United Kingdom

We report the results of lattice numerical studies of the Sp(4) gauge theory coupled to fermions (hyperquarks) transforming in the fundamental and two-index antisymmetric representations of the gauge group. This strongly coupled theory is the minimal candidate for the ultraviolet completion of composite Higgs models that facilitate the mechanism of partial compositeness for generating the top-quark mass. We measure the spectrum of the low-lying, half-integer spin, bound states composed of two fundamental and one antisymmetric hyperquarks, dubbed chimera baryons, in the quenched approximation. In this first systematic, nonperturbative study, we focus on the three lightest parity-even chimera-baryon states, in analogy with QCD, denoted as ΛCB, ΣCB (both with spin 1/2), and ΣCB* (with spin 3/2). The spin-1/2 such states are candidates of the top partners. The extrapolation of our results to the continuum and massless-hyperquark limit is performed using formulas inspired by QCD heavy-baryon Wilson chiral perturbation theory. Within the range of hyperquark masses in our simulations, we find that ΣCB is not heavier than ΛCB.

关键词： Composite models Extensions of Higgs sector Lattice field theory Strong interaction

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