Classification of lung cancer subtypes on CT images with synthetic pathological priors

• URL: http://arxiv.org/abs/2308.04663v1
• Date: Wed, 9 Aug 2023 02:04:05 GMT
• Title: Classification of lung cancer subtypes on CT images with synthetic pathological priors
• Authors: Wentao Zhu and Yuan Jin and Gege Ma and Geng Chen and Jan Egger and Shaoting Zhang and Dimitris N. Metaxas
• Abstract summary: Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images. We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
• Score: 41.75054301525535
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score.

Related papers

• CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images [13.655407979403945]
  We propose a novel deep learning network designed to accurately classify lung cancer subtype with multi-dimensional and multi-modality images. The strength of the proposed model lies in its ability to dynamically process both paired CT-pathological image sets and independent CT image sets. We also develop a contrastive constraint module, which quantitatively maps the cross-modality associations through network training.
  arXiv  Detail & Related papers  (2024-07-18T01:42:00Z)
• A Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis with Incomplete Data [18.15801599933636]
  We propose a unified framework for kidney cancer diagnosis with incomplete multi-phase CT. It simultaneously recovers missing CT images and classifies cancer subtypes using the completed set of images. The proposed framework is based on fully 3D convolutional neural networks.
  arXiv  Detail & Related papers  (2023-12-09T11:34:14Z)
• Histopathologic Cancer Detection [0.0]
  This work uses the PatchCamelyon benchmark datasets and trains them in a multi-layer perceptron and convolution model to observe the model's performance in terms of precision Recall, F1 Score, Accuracy, and AUC Score. Also, this paper introduced ResNet50 and InceptionNet models with data augmentation, where ResNet50 is able to beat the state-of-the-art model.
  arXiv  Detail & Related papers  (2023-11-13T19:51:46Z)
• High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model [10.412300404240751]
  Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years. Deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the screening process. However, developing robust and accurate models often requires large-scale and diverse medical datasets with high-quality annotations.
  arXiv  Detail & Related papers  (2023-05-02T01:04:22Z)
• Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models [7.745729132928934]
  We propose to use an emerging deep learning framework called diffusion and score-matching models. Our results show that the diffusion and score-matching models generate better synthetic CT images than the CNN and GAN models. Our study suggests that diffusion and score-matching models are powerful to generate high quality images conditioned on an image obtained using a complementary imaging modality.
  arXiv  Detail & Related papers  (2022-09-24T23:50:54Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• 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)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)

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.