Private, Efficient and Scalable Kernel Learning for Medical Image Analysis

• URL: http://arxiv.org/abs/2410.15840v1
• Date: Mon, 21 Oct 2024 10:03:03 GMT
• Title: Private, Efficient and Scalable Kernel Learning for Medical Image Analysis
• Authors: Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal, Mete Akgün,
• Abstract summary: OKRA (Orthonormal K-fRAmes) is a novel randomized encoding-based approach for kernel-based machine learning. It significantly enhances scalability and speed compared to current state-of-the-art solutions.
• Score: 1.7999333451993955
• License:
• Abstract: Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights into patient health. To predict diseases or provide individualized therapies, machine learning techniques like kernel methods have been widely used. Nevertheless, there are multiple challenges for implementing kernel methods. Medical image data often originates from various hospitals and cannot be combined due to privacy concerns, and the high dimensionality of image data presents another significant obstacle. While randomised encoding offers a promising direction, existing methods often struggle with a trade-off between accuracy and efficiency. Addressing the need for efficient privacy-preserving methods on distributed image data, we introduce OKRA (Orthonormal K-fRAmes), a novel randomized encoding-based approach for kernel-based machine learning. This technique, tailored for widely used kernel functions, significantly enhances scalability and speed compared to current state-of-the-art solutions. Through experiments conducted on various clinical image datasets, we evaluated model quality, computational performance, and resource overhead. Additionally, our method outperforms comparable approaches

Related papers

• Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection [11.980634373191542]
  Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis. This paper presents an innovative approach to medical image classification, leveraging Federated Learning (FL) to address the dual challenges of data privacy and efficient disease diagnosis.
  arXiv  Detail & Related papers  (2024-04-15T09:07:19Z)
• Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping [3.550171634694342]
  Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude.
  arXiv  Detail & Related papers  (2024-02-28T00:57:35Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• A Segmentation Method for fluorescence images without a machine learning approach [0.0]
  This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets.
  arXiv  Detail & Related papers  (2022-12-28T16:47:05Z)
• FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain [0.8376091455761259]
  A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance.
  arXiv  Detail & Related papers  (2022-08-30T16:06:07Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information [3.7273619690170796]
  Anomaly detection in medical imaging is to distinguish the relevant biomarkers of diseases from those of normal tissues. Deep supervised learning methods have shown potentials in various detection tasks, but its performances would be limited in medical imaging fields. In this paper, we developed an unsupervised learning framework for pixel-wise anomaly detection in multi-contrast MRI.
  arXiv  Detail & Related papers  (2021-05-02T13:05:36Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
  In hospitals, data are siloed to specific information systems that make the same information available under different modalities. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time. We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
  arXiv  Detail & Related papers  (2020-10-20T20:05:35Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.