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Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer

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arXiv 2025年

作者： Caragliano, Alice Natalina Ruffini, Filippo Greco, Carlo Ippolito, Edy Fiore, Michele Tacconi, Claudia Nibid, Lorenzo Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Research Unit of Radiation Oncology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy Operative Research Unit of Radiation Oncology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Research Unit of Anatomical Pathology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy Anatomical Pathology Operative Research Unit Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predictions to guide personalized treatments. Although artificial intelligence models show promise in this domain, their clinical adoption is limited by the lack of medically grounded guidance during training, often resulting in non-explainable intrinsic predictions. To address this, we propose Doctor-in-the-Loop, a novel framework that integrates expert-driven domain knowledge with explainable artificial intelligence techniques, directing the model toward clinically relevant anatomical regions and improving both interpretability and trustworthiness. Our approach employs a gradual multi-view strategy, progressively refining the model’s focus from broad contextual features to finer, lesion-specific details. By incorporating domain insights at every stage, we enhance predictive accuracy while ensuring that the model’s decision-making process aligns more closely with clinical reasoning. Evaluated on a dataset of NSCLC patients, Doctor-in-the-Loop delivers promising predictive performance and provides transparent, justifiable outputs, representing a significant step toward clinically explainable artificial intelligence in oncology. © 2025, CC BY-NC-ND.

关键词： Lung cancer

Early Experiences on using Triplet Networks for Histological Subtype Classification in Non-Small Cell Lung Cancer

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Early Experiences on using Triplet Networks for Histological...

Annual IEEE Symposium on computer-Based Medical systems

作者： Fatih Aksu Fabrizia Gelardi Arturo Chiti Paolo Soda Department of Biomedical Sciences Humanitas University Milan Italy IRCCS Humanitas Research Hospital Milan Italy Research Unit of Computer Systems and Bioinformatics Campus Bio-Medico University of Rome Rome Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Lung cancer has the highest mortality rate among tumours and an accurate pathological assessment is crucial to deliver personalized treatments to patients. The gold standard for pathological assessment requires invasive procedures, which are not always possible and might cause clinical complications. Therefore, in the last years, efforts have been directed towards the development of machine and deep learning approaches for virtual biopsy, which leverage routinely collected CT scans. However, in many cases, the available datasets are limited in size, an issue that limits the training of any model. In this paper, we investigate if triplet networks can cope with this limitation: they are a class of neural networks that uses the same weights while working in tandem on three different input vectors to minimize the loss function. In particular, on a dataset including 87 CT scans collected from patients suffering from non-small cell lung cancer, we experimentally compare triplet networks against plain deep networks when performing histological subtype classification. The results show that the former outperforms the latter in almost all experiments.

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

Few-Shot Fruit Segmentation via Transfer Learning

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arXiv 2024年

作者： James, Jordan A. Manching, Heather K. Hulse-Kemp, Amanda M. Beksi, William J. The Department of Computer Science and Engineering The University of Texas at Arlington ArlingtonTX United States The Department of Crop and Soil Sciences North Carolina State University RaleighNC United States The Genomics and Bioinformatics Research Unit USDA Agricultural Research Service RaleighNC United States

Advancements in machine learning, computer vision, and robotics have paved the way for transformative solutions in various domains, particularly in agriculture. For example, accurate identification and segmentation of fruits from field images plays a crucial role in automating jobs such as harvesting, disease detection, and yield estimation. However, achieving robust and precise infield fruit segmentation remains a challenging task since large amounts of labeled data are required to handle variations in fruit size, shape, color, and occlusion. In this paper, we develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Concretely, our work is aimed at addressing agricultural domains that lack publicly available labeled data. Motivated by similar success in urban scene parsing, we propose specialized pre-training using a public benchmark dataset for fruit transfer learning. By leveraging pre-trained neural networks, accurate semantic segmentation of fruit in the field is achieved with only a few labeled images. Furthermore, we show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground, and they can effectively transfer the knowledge to the target fruit dataset. © 2024, CC BY.

关键词： Object detection

MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation

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arXiv 2025年

作者： Molino, Daniele Feola, Francesco Di Faiella, Eliodoro Fazzini, Deborah Santucci, Domiziana Shen, Linlin Guarrasi, Valerio Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Roma Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Milano Italy Department of Radiology and Interventional Radiology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Research Unit of Radiology and Interventional Radiology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Artificial Intelligence is revolutionizing medical practice, enhancing diagnostic accuracy and healthcare delivery. However, its adaptation in medical settings still faces significant challenges, related to data availability and privacy constraints. Synthetic data has emerged as a promising solution to mitigate these issues, addressing data scarcity while preserving privacy. Recently, Latent Diffusion Models have emerged as a powerful tool for generating high-quality synthetic data. Meanwhile, the integration of different modalities has gained interest, emphasizing the need of models capable of handle multimodal medical data. Existing approaches struggle to integrate complementary information and lack the ability to generate modalities simultaneously. To address this challenge, we present MedCoDi-M, a 6.77-billion-parameter model, designed for multimodal medical data generation, that, following Foundation Model paradigm, exploits contrastive learning and large quantity of data to build a shared latent space which capture the relationships between different data modalities. Further, we introduce the Multi-Prompt training technique, which significantly boosts MedCoDi-M’s generation under different settings. We extensively validate MedCoDi-M: first we benchmark it against five competitors on the MIMIC-CXR dataset, a state-of-the-art dataset for Chest X-ray and radiological report generation. Secondly, we perform a Visual Turing Test with expert radiologists to assess the realism and clinical relevance of the generated data, ensuring alignment with real-world scenarios. Finally, we assess the utility of MedCoDi-M in addressing key challenges in the medical field, such as anonymization, data scarcity and imbalance learning. The results are promising, demonstrating the applicability of MedCoDi-M in medical contexts. Project page is at https://***/MedCoDi-M/. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

Ideation of engineering Solutions for Sustainable Development Goals: A Collaborative Course Between Thai and Japanese Students 38

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Ideation of Engineering Solutions for Sustainable Developmen...

38th IEEE Region 10 Conference, TENCON 2023

作者： Krutphong, Kodchakorn Leelawat, Natt Tang, Jing Ota, Eri Suzuki, Rie Murakami Taoka, Yuki Punyabukkana, Proadpran Environmental Engineering and Disaster Management Program Mahidol University Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Thailand International School of Engineering Chulalongkorn University Thailand Center for International Education Tokyo Institute of Technology Japan School of Environmet and Society Tokyo Institute of Technology Japan Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand The Learning Innovation Center Chulalongkorn University Thailand

ISBN: (纸本)9798350302196

A collaborative course called Global Awareness for Technology Implementation was conducted by the Faculty of engineering, Chulalongkorn University, Thailand, and the Tokyo Institute of Technology, Japan. The objective of this course was to facilitate the exchange of knowledge, perspectives, and experiences between Thai and Japanese students through project work, using an engineering design approach and the design thinking process to create innovation relevant to the Sustainable Development Goals (SDGs). During the class, experts shared their knowledge and experiences with the students. The students then worked on their respective projects to develop solution concepts using the design thinking process. The proposed ideas include the application for English class, smart water quality checker, hydrogen-based energy system for rural area, reduced wasted fashion solution, and auto waste sorting machine. Finally, this study concluded with a summary of the results and findings on the empathy process in the chosen SDGs, and of the commonalities and differences between Thailand and Japan in terms of ideation. © 2023 IEEE.

关键词： Students

Towards Consumer-Oriented Demand Response systems 7

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Towards Consumer-Oriented Demand Response Systems

7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Cortez, Vitor Rabelo, Ricardo Carvalho, Arthur Pilloni, Virginia Department of Computer Science Federal University of Piaui PI Teresina Brazil Miami University Department of Information Systems and Analytics Farmer School of Business OxfordOH United States Research Unit of Cagliari Department of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

ISBN: (纸本)9789532901160

Consumer acceptance is a must-have for deploying and managing successful demand response systems. Yet, recent research trends favor automated solutions that consider end consumers as passive agents. This paper explores the notion of considering consumers as active agents that interfere with the optimal plan of a demand response system. We simulate an energy community by implementing a state-of-the-art demand response system. Active consumers are modeled to be inflexible using multi-criteria decision-making methods. Results show that the greater the number of inflexible consumers, the greater the impact on the shape of the community's aggregate demand profile. Further studies should consider the impact of this cost and inflexible consumers in their numerical analysis. © 2022 University of Split, FESB.

关键词： Smart power grids

Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

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Drone Application Model for Image Acquisition of Plantation ...

7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence

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arXiv 2025年

作者： Rofena, Aurora Piccolo, Claudia Lucia Zobel, Bruno Beomonte Soda, Paolo Guarrasi, Valerio Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Radiology Fondazione Policlinico Campus Bio-Medico Rome Italy Department of Radiology Università Campus Bio-Medico di Roma Rome Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening;however, its effectiveness is limited in patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced Spectral Mammography (CESM), a second-level imaging technique, offers enhanced accuracy in tumor detection. Nonetheless, its application is restricted due to higher radiation exposure, the use of contrast agents, and limited accessibility. As a result, CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM despite the superior diagnostic performance of CESM. While biopsy remains the gold standard for definitive diagnosis, it is an invasive procedure that can cause discomfort for patients. We introduce a multimodal, multi-view deep learning approach for virtual biopsy, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign. To address the challenge of missing CESM data, we leverage generative artificial intelligence to impute CESM images from FFDM scans. Experimental results demonstrate that incorporating the CESM modality is crucial to enhance the performance of virtual biopsy. When real CESM data is missing, synthetic CESM images proved effective, outperforming the use of FFDM alone, particularly in multimodal configurations that combine FFDM and CESM modalities. The proposed approach has the potential to improve diagnostic workflows, providing clinicians with augmented intelligence tools to improve diagnostic accuracy and patient care. Additionally, as a contribution to the research community, we publicly release the dataset used in our experiments, facilitating further advancements in this field. Copyright © 2025, The Authors. All rights reserved.

关键词： Biopsy