Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques

• URL: http://arxiv.org/abs/2406.13441v1
• Date: Wed, 19 Jun 2024 11:07:55 GMT
• Title: Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques
• Authors: Miguel Nogales, Begoña Acha, Fernando Alarcón, José Pereyra, Carmen Serrano,
• Abstract summary: This study focuses on analyzing dermoscopy images to determine the depth of melanomas. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. Results indicated that the models achieved significant improvements over previous methods.
• Score: 39.97900702763419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study focuses on analyzing dermoscopy images to determine the depth of melanomas, which is a critical factor in diagnosing and treating skin cancer. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. This research aims to improve the prediction of the depth of melanoma through the use of machine learning models, specifically deep learning, while also providing an analysis of the possible existance of graduation in the images characteristics which correlates with the depth of the melanomas. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. The datasets were combined and balanced to ensure robust model training. The study utilized pre-trained Convolutional Neural Networks (CNNs). Results indicated that the models achieved significant improvements over previous methods. Additionally, the study conducted a correlation analysis between model's predictions and actual melanoma thickness, revealing a moderate correlation that improves with higher thickness values. Explainability methods such as feature visualization through Principal Component Analysis (PCA) demonstrated the capability of deep features to distinguish between different depths of melanoma, providing insight into the data distribution and model behavior. In summary, this research presents a dual contribution: enhancing the state-of-the-art classification results through advanced training techniques and offering a detailed analysis of the data and model behavior to better understand the relationship between dermoscopy images and melanoma thickness.

Related papers

• A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images [0.0]
  This paper presents a system that combines deep learning techniques with established transfer learning methods to enable skin lesions classification and diagnosis of melanoma skin lesions. Researchers used 'Deep Learning' techniques to train an expansive number of photos & essentially to get the expected result. DenseNet performed better than the others which gives a validation accuracy of 96.64%, validation loss of 9.43% & test set accuracy of 99.63%.
  arXiv  Detail & Related papers  (2023-12-02T19:46:48Z)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows [22.335623464185105]
  Melanoma is a serious form of skin cancer with high mortality rate at later stages. datasets are heavily imbalanced which complicates training current state-of-the-art supervised classification AI models. We propose to use generative models to learn the benign data distribution and detect Out-of-Distribution (OOD) malignant images through density estimation.
  arXiv  Detail & Related papers  (2022-08-09T09:57:56Z)
• Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images [0.0]
  We evaluate attribution methods for illuminating how deep neural networks analyze medical images. We attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.
  arXiv  Detail & Related papers  (2022-08-01T16:05:14Z)
• Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders [65.95959936242993]
  We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent skeletal related events (SREs) in breast cancer patients.
  arXiv  Detail & Related papers  (2022-03-20T21:00:10Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Meta-learning for skin cancer detection using Deep Learning Techniques [0.0]
  This study focuses on automatic skin cancer detection using a Meta-learning approach for dermoscopic images. A small sample of a combined dataset from 3 different sources was used to fine-tune a ResNet model pre-trained on non-medical data.
  arXiv  Detail & Related papers  (2021-04-21T21:44:25Z)
• An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI [1.0948946179065253]
  Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. We propose a novel end-to-end weakly-supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN) The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy.
  arXiv  Detail & Related papers  (2021-04-20T10:18:57Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

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.