Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions

• URL: http://arxiv.org/abs/2311.15719v1
• Date: Mon, 27 Nov 2023 11:12:33 GMT
• Title: Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
• Authors: Benjamin Keel, Aaron Quyn, David Jayne, Samuel D. Relton
• Abstract summary: Lung cancer is responsible for 21% of cancer deaths in the UK. Recent studies have demonstrated the capability of AI methods for accurate and early diagnosis of lung cancer from routine scans. This study investigates the application Variational Autoencoders (VAEs), a type of generative AI model, to lung cancer lesions.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Lung cancer is responsible for 21% of cancer deaths in the UK and five-year survival rates are heavily influenced by the stage the cancer was identified at. Recent studies have demonstrated the capability of AI methods for accurate and early diagnosis of lung cancer from routine scans. However, this evidence has not translated into clinical practice with one barrier being a lack of interpretable models. This study investigates the application Variational Autoencoders (VAEs), a type of generative AI model, to lung cancer lesions. Proposed models were trained on lesions extracted from 3D CT scans in the LIDC-IDRI public dataset. Latent vector representations of 2D slices produced by the VAEs were explored through clustering to justify their quality and used in an MLP classifier model for lung cancer diagnosis, the best model achieved state-of-the-art metrics of AUC 0.98 and 93.1% accuracy. Cluster analysis shows the VAE latent space separates the dataset of malignant and benign lesions based on meaningful feature components including tumour size, shape, patient and malignancy class. We also include a comparative analysis of the standard Gaussian VAE (GVAE) and the more recent Dirichlet VAE (DirVAE), which replaces the prior with a Dirichlet distribution to encourage a more explainable latent space with disentangled feature representation. Finally, we demonstrate the potential for latent space traversals corresponding to clinically meaningful feature changes.

Related papers

• Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection [8.224446601436757]
  We propose a novel 2.5D cross-slice attention model that utilizes both global and local information, along with an evidential critical loss. We perform extensive experiments with our model on two different datasets and achieve state-of-the-art performance in prostate cancer detection.
  arXiv  Detail & Related papers  (2024-07-01T10:14:23Z)
• Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
  We propose a novel text-guided learning method to achieve highly accurate cancer detection results. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
  arXiv  Detail & Related papers  (2024-05-23T07:03:38Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Double Integral Enhanced Zeroing Neural Network Optimized with ALSOA fostered Lung Cancer Classification using CT Images [1.1510009152620668]
  Lung cancer is one of the deadliest diseases and the leading cause of illness and death. The proposed method attains 18.32%, 27.20%, and 34.32% higher accuracy analyzed with existing method.
  arXiv  Detail & Related papers  (2023-12-05T10:53:35Z)
• Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis [20.510918720980467]
  Lung cancer has the highest mortality rate of deadly cancers in the world. Computer-aided diagnosis (CAD) systems have been developed to assist radiologists in nodule detection and diagnosis. Lack of model reliability and interpretability remains a major obstacle for its large-scale clinical application.
  arXiv  Detail & Related papers  (2022-04-08T08:21:00Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images [9.525711971667679]
  Lung cancer is the leading cause of cancer death worldwide. Deep learning techniques are effective at automatically diagnosing lung cancer. These techniques have yet to be clinically approved and adopted by the medical community.
  arXiv  Detail & Related papers  (2020-12-10T04:11:59Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms [0.4297070083645048]
  We present a novel generative adversarial network (GAN) model for data augmentation that can realistically synthesize and remove lesions on mammograms. With self-attention and semi-supervised learning components, the U-net-based architecture can generate high resolution (256x256px) outputs.
  arXiv  Detail & Related papers  (2020-05-29T21:23:00Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

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.