Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging

• URL: http://arxiv.org/abs/2402.15246v1
• Date: Fri, 23 Feb 2024 10:21:03 GMT
• Title: Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging
• Authors: Adri Gomez Martin, Carlos Fernandez del Cerro, Monica Abella Garcia and Manuel Desco Menendez
• Abstract summary: We propose a novel, hybrid neuroevolutive algorithm that integrates the Artificial Bee Colony Algorithm with Evolutionary Computation tools to generate models from scratch. The Chimera Algorithm has been validated with two datasets of natural and medical images, producing models that surpassed the performance of those coming from Transfer Learning.
• Score: 9.334663477968027
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most efforts in Computer Vision focus on natural images or artwork, which differ significantly both in size and contents from the kind of data biomedical image processing deals with. Thus, Transfer Learning models often prove themselves suboptimal for these tasks, even after manual finetuning. The development of architectures from scratch is oftentimes unfeasible due to the vastness of the hyperparameter space and a shortage of time, computational resources and Deep Learning experts in most biomedical research laboratories. An alternative to manually defining the models is the use of Neuroevolution, which employs metaheuristic techniques to optimize Deep Learning architectures. However, many algorithms proposed in the neuroevolutive literature are either too unreliable or limited to a small, predefined region of the hyperparameter space. To overcome these shortcomings, we propose the Chimera Algorithm, a novel, hybrid neuroevolutive algorithm that integrates the Artificial Bee Colony Algorithm with Evolutionary Computation tools to generate models from scratch, as well as to refine a given previous architecture to better fit the task at hand. The Chimera Algorithm has been validated with two datasets of natural and medical images, producing models that surpassed the performance of those coming from Transfer Learning.

Related papers

• An automatic selection of optimal recurrent neural network architecture for processes dynamics modelling purposes [0.0]
  The research has included four original proposals of algorithms dedicated to neural network architecture search. Algorithms have been based on well-known optimisation techniques such as evolutionary algorithms and gradient descent methods. The research involved an extended validation study based on data generated from a mathematical model of the fast processes occurring in a pressurised water nuclear reactor.
  arXiv  Detail & Related papers  (2023-09-25T11:06:35Z)
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
  We propose a domain transfer approach based on conditional invertible neural networks (cINNs) Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood. Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
  arXiv  Detail & Related papers  (2023-03-17T18:00:27Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Multi-level Data Representation For Training Deep Helmholtz Machines [0.0]
  We guide the learning of a biologically plausible generative model called the Helmholtz Machine in complex search spaces using the Human Image Perception mechanism. We propose to overcome this problem, by providing the network's hidden layers with visual queues at different resolutions using a Multi-level Data representation. The results on several image datasets showed the model was able to not only obtain better overall quality but also a wider diversity in the generated images.
  arXiv  Detail & Related papers  (2022-10-26T16:55:40Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection [0.0]
  The work concentrates on improvements to multi-level neuroevolution approach for anomaly detection. The presented framework can be used as an efficient learning network architecture method for any different unsupervised task.
  arXiv  Detail & Related papers  (2021-12-10T16:14:43Z)
• Improving RNA Secondary Structure Design using Deep Reinforcement Learning [69.63971634605797]
  We propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches.
  arXiv  Detail & Related papers  (2021-11-05T02:54:06Z)
• On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future [60.99717891994599]
  We propose an approach that extracts information from neuroevolutionary runs, and use it to build a metamodel. We inspect the best structures found during neuroevolutionary searches of generative adversarial networks with varying characteristics.
  arXiv  Detail & Related papers  (2021-05-26T20:55:29Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Evolutionary Neural Architecture Search for Retinal Vessel Segmentation [2.0159253466233222]
  We propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation. A modified evolutionary algorithm is used to evolve the architectures of encoder-decoder framework with limited computing resources. The results of cross-training show that the evolved model is with considerable scalability, which indicates a great potential for clinical disease diagnosis.
  arXiv  Detail & Related papers  (2020-01-18T15:07:26Z)

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.