Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement

• URL: http://arxiv.org/abs/2402.19001v3
• Date: Sun, 14 Apr 2024 21:13:01 GMT
• Title: Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement
• Authors: Xinyi Fang, Xu Yang, Chak Fong Chong, Kei Long Wong, Yapeng Wang, Tiankui Zhang, Sio-Kei Im,
• Abstract summary: This work pioneers exploring two-step heterogeneous transfer learning (THTL) for laryngeal lesion classification. We use diabetic retinopathy color fundus images, semantically non-identical yet vascular images, as the intermediate domain. We introduce an enhanced fine-tuning strategy in THTL called Step-Wise Fine-Tuning (SWFT) and apply it to the ResNet models.
• Score: 8.7937485450551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate classification of laryngeal vascular as benign or malignant is crucial for early detection of laryngeal cancer. However, organizations with limited access to laryngeal vascular images face challenges due to the lack of large and homogeneous public datasets for effective learning. Distinguished from the most familiar works, which directly transfer the ImageNet pre-trained models to the target domain for fine-tuning, this work pioneers exploring two-step heterogeneous transfer learning (THTL) for laryngeal lesion classification with nine deep-learning models, utilizing the diabetic retinopathy color fundus images, semantically non-identical yet vascular images, as the intermediate domain. Attention visualization technique, Layer Class Activate Map (LayerCAM), reveals a novel finding that yet the intermediate and the target domain both reflect vascular structure to a certain extent, the prevalent radial vascular pattern in the intermediate domain prevents learning the features of twisted and tangled vessels that distinguish the malignant class in the target domain, summarizes a vital rule for laryngeal lesion classification using THTL. To address this, we introduce an enhanced fine-tuning strategy in THTL called Step-Wise Fine-Tuning (SWFT) and apply it to the ResNet models. SWFT progressively refines model performance by accumulating fine-tuning layers from back to front, guided by the visualization results of LayerCAM. Comparison with the original THTL approach shows significant improvements. For ResNet18, the accuracy and malignant recall increases by 26.1% and 79.8%, respectively, while for ResNet50, these indicators improve by 20.4% and 62.2%, respectively.

Related papers

• Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images [0.0]
  Research focuses on the critical field of early diagnosis of disease by examining retinal blood vessels in fundus images. Our research in fundus image analysis advances deep learning-based classification using eight pre-trained CNN models. To enhance interpretability, we utilize Explainable AI techniques such as Grad-CAM, Grad-CAM++, Score-CAM, Faster Score-CAM, and Layer CAM.
  arXiv  Detail & Related papers  (2024-05-12T17:21:57Z)
• Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin [0.0]
  Identifying the thromboembolism source in ischemic stroke is crucial for treatment and secondary prevention. This study describes a self-supervised deep learning approach in digital pathology of emboli for classifying ischemic stroke clot origin.
  arXiv  Detail & Related papers  (2024-05-01T23:40:12Z)
• Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study [9.161480191416551]
  We compare various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. A semi-supervised learning approach is proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images. The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning.
  arXiv  Detail & Related papers  (2023-09-29T13:22:17Z)
• Deep Angiogram: Trivializing Retinal Vessel Segmentation [1.8479315677380455]
  We propose a contrastive variational auto-encoder that can filter out irrelevant features and synthesize a latent image, named deep angiogram. The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features.
  arXiv  Detail & Related papers  (2023-07-01T06:13:10Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification [30.30848090813239]
  We propose a vessel-mixing based consistency regularization framework, for cross-domain learning in retinal A/V classification. Our method achieves the state-of-the-art cross-domain performance, which is also close to the upper bound obtained by fully supervised learning on target domain.
  arXiv  Detail & Related papers  (2021-03-16T14:18:35Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.