Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images

• URL: http://arxiv.org/abs/2012.05447v2
• Date: Thu, 17 Dec 2020 08:57:50 GMT
• Title: Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images
• Authors: Michael J. Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Douglas P. S. Gomes, Anwaar Ul-Haq
• Abstract summary: 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.
• Score: 9.525711971667679
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lung cancer is the leading cause of cancer death worldwide with early detection being the key to a positive patient prognosis. Although a multitude of studies have demonstrated that machine learning, and particularly deep learning, techniques are effective at automatically diagnosing lung cancer, these techniques have yet to be clinically approved and adopted by the medical community. Most research in this field is focused on the narrow task of nodule detection to provide an artificial radiological second reading. We instead focus on extracting, from chest X-ray images, a wider range of pathologies associated with lung cancer using a computer vision model trained on a large dataset. We then find the set of best fit decision trees against an independent, smaller dataset for which lung cancer malignancy metadata is provided. For this small inferencing dataset, our best model achieves sensitivity and specificity of 85% and 75% respectively with a positive predictive value of 85% which is comparable to the performance of human radiologists. Furthermore, the decision trees created by this method may be considered as a starting point for refinement by medical experts into clinically usable multi-variate lung cancer scoring and diagnostic models.

Related papers

• 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)
• Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion [68.45407109385306]
  In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022. There has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data. In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy.
  arXiv  Detail & Related papers  (2023-11-30T15:11:03Z)
• Leveraging object detection for the identification of lung cancer [0.15229257192293202]
  The YOLOv5 model was employed to train an algorithm capable of detecting cancerous lung lesions. The trained YOLOv5 model exhibited exceptional proficiency in identifying lung cancer lesions, displaying high accuracy and recall rates.
  arXiv  Detail & Related papers  (2023-05-25T07:53:18Z)
• Artificial intelligence based prediction on lung cancer risk factors using deep learning [0.0]
  Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. We developed a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%.
  arXiv  Detail & Related papers  (2023-04-11T08:57:15Z)
• Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection [52.93342510469636]
  Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation.
  arXiv  Detail & Related papers  (2022-07-19T16:38:48Z)
• Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning [0.9786690381850355]
  We introduce a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. Our model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively.
  arXiv  Detail & Related papers  (2022-06-02T15:14:41Z)
• Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection [11.228544549618068]
  Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. We show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset.
  arXiv  Detail & Related papers  (2022-01-24T10:08:07Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• 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)
• Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation [0.0]
  We present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSCR of 97.5% on the JSRT dataset.
  arXiv  Detail & Related papers  (2020-03-23T14:45:48Z)

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.