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Artificial Intelligence in Medicine

22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Joseph Finkelstein

    1. University of Utah, Salt Lake City, USA

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  2. Robert Moskovitch

    1. Ben-Gurion University of the Negev, Beer Sheva, Israel

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    You can also search for this editor in PubMed   Google Scholar

  3. Enea Parimbelli

    1. University of Pavia, Pavia, Italy

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14845)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: AIME 2024.

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About this book

This two-volume set LNAI 14844-14845 constitutes the refereed proceedings of the 22nd International Conference on Artificial Intelligence in Medicine, AIME 2024, held in Salt Lake City, UT, USA, during July 9-12, 2024.

The 54 full papers and 22 short papers presented in the book were carefully reviewed and selected from 335 submissions.

The papers are grouped in the following topical sections:

Part I: Predictive modelling and disease risk prediction; natural language processing; bioinformatics and omics; and wearable devices, sensors, and robotics.

Part II: Medical imaging analysis; data integration and multimodal analysis; and explainable AI.

Keywords

Table of contents (36 papers)

  1. Front Matter

    Pages i-xxvii
    Download chapter PDF

  2. Medical Imaging Analysis

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. 3T to 7T Whole Brain + Skull MRI Translation with Densely Engineered U-Net Network

      • Aryan Kalluvila, Matthew S. Rosen
      Pages 3-8

    3. A Sparse Convolutional Autoencoder for Joint Feature Extraction and Clustering of Metastatic Prostate Cancer Images

      • Zhijun Chen, Erolcan Sayar, Haoyue Zhang, Helen Richards, Lucas Liu, Baris Turkbey et al.
      Pages 9-20

    4. AI in Neuro-Oncology: Predicting EGFR Amplification in Glioblastoma from Whole Slide Images Using Weakly Supervised Deep Learning

      • Homay Danaei Mehr, Imran Noorani, Priyanka Rana, Antonio Di Ieva, Sidong Liu
      Pages 21-29

    5. An Exploration of Diabetic Foot Osteomyelitis X-ray Data for Deep Learning Applications

      • Brandon Abela, Martin Masek, Jumana Abu-Khalaf, David Suter, Ashu Gupta
      Pages 30-39

    6. Automated Detection and Characterization of Small Cell Lung Cancer Liver Metastases on CT

      • Sophia Ty, Fahmida Haque, Parth Desai, Nobuyuki Takahashi, Usamah Chaudhary, Benjamin Simon et al.
      Pages 40-44

    7. Content-Based Medical Image Retrieval for Medical Radiology Images

      • Dario Barać, Teo Manojlović, Mateja Napravnik, Franko Hržić, Mihaela Mamula Saračević, Damir Miletić et al.
      Pages 45-59

    8. Cross-Modality Synthesis of T1c MRI from Non-contrast Images Using GANs: Implications for Brain Tumor Research

      • Mehnaz Tabassum, Priyanka Rana, Eric Suero Molina, Antonio Di Ieva, Sidong Liu
      Pages 60-69

    9. Harnessing the Power of Graph Propagation in Lung Nodule Detection

      • Sudipta Modak, Yash Trivedi, Esam Abdel-Raheem, Luis Rueda
      Pages 70-80

    10. Histology Image Artifact Restoration with Lightweight Transformer Based Diffusion Model

      • Chong Wang, Zhenqi He, Junjun He, Jin Ye, Yiqing Shen
      Pages 81-89

    11. Improved Glioma Grade Prediction with Mean Image Transformation

      • Shyam Sundhar Yathirajam, Sreedevi Gutta
      Pages 90-94

    12. Learning to Predict the Optimal Template in Stain Normalization for Histology Image Analysis

      • Shiling Luo, Junxin Feng, Yiqing Shen, Qiongxiong Ma
      Pages 95-103

    13. MRI Brain Cancer Image Detection: Application of an Integrated U-Net and ResNet50 Architecture

      • Mahshid Benchari, Michael W. Totaro
      Pages 104-108

    14. MRI Scan Synthesis Methods Based on Clustering and Pix2Pix

      • Giulia Baldini, Melanie Schmidt, Charlotte Zäske, Liliana L. Caldeira
      Pages 109-125

    15. Supervised Pectoral Muscle Removal in Mammography Images

      • Parvaneh Aliniya, Mircea Nicolescu, Monica Nicolescu, George Bebis
      Pages 126-130

    16. TinySAM-Med3D: A Lightweight Segment Anything Model for Volumetric Medical Imaging with Mixture of Experts

      • Tianyuan Song, Guixia Kang, Yiqing Shen
      Pages 131-139

    17. Towards a Formal Description of Artificial Intelligence Models and Datasets in Radiology

      • Charles E. Kahn Jr., Abhinav Suri, Safwan Halabi, Hari Trivedi
      Pages 140-144

    18. Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification

      • Timo Löhr, Michael Ingrisch, Eyke Hüllermeier
      Pages 145-155

    19. Ultrasound Image Segmentation via a Multi-scale Salient Network

      • Abdalrahman Alblwi, Kenneth E. Barner
      Pages 156-161
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Other volumes

  1. Artificial Intelligence in Medicine

  2. Artificial Intelligence in Medicine

Editors and Affiliations

  • University of Utah, Salt Lake City, USA

    Joseph Finkelstein

  • Ben-Gurion University of the Negev, Beer Sheva, Israel

    Robert Moskovitch

  • University of Pavia, Pavia, Italy

    Enea Parimbelli

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