Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning

• URL: http://arxiv.org/abs/2008.01158v3
• Date: Wed, 17 Nov 2021 02:42:07 GMT
• Title: Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning
• Authors: Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, Joseph Y. Lo
• Abstract summary: Rule-based algorithms were used to extract 19,225 disease labels from 13,667 body CT scans from 12,092 patients. For each organ, a three-dimensional convolutional neural network classified no apparent disease versus four common diseases for a total of 15 different labels. Results: Manual validation of the extracted labels confirmed 91% to 99% accuracy across the 15 different labels.
• Score: 7.287303475865695
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.Materials & Methods: This retrospective study included a total of 12,092 patients (mean age 57 +- 18; 6,172 women) for model development and testing (from 2012-2017). Rule-based algorithms were used to extract 19,225 disease labels from 13,667 body CT scans from 12,092 patients. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura; liver and gallbladder; and kidneys and ureters. For each organ, a three-dimensional convolutional neural network classified no apparent disease versus four common diseases for a total of 15 different labels across all three models. Testing was performed on a subset of 2,158 CT volumes relative to 2,875 manually derived reference labels from 2133 patients (mean age 58 +- 18;1079 women). Performance was reported as receiver operating characteristic area under the curve (AUC) with 95% confidence intervals by the DeLong method. Results: Manual validation of the extracted labels confirmed 91% to 99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were: atelectasis 0.77 (95% CI: 0.74, 0.81), nodule 0.65 (0.61, 0.69), emphysema 0.89 (0.86, 0.92), effusion 0.97 (0.96, 0.98), and no apparent disease 0.89 (0.87, 0.91). AUCs for liver and gallbladder were: hepatobiliary calcification 0.62 (95% CI: 0.56, 0.67), lesion 0.73 (0.69, 0.77), dilation 0.87 (0.84, 0.90), fatty 0.89 (0.86, 0.92), and no apparent disease 0.82 (0.78, 0.85). AUCs for kidneys and ureters were: stone 0.83 (95% CI: 0.79, 0.87), atrophy 0.92 (0.89, 0.94), lesion 0.68 (0.64, 0.72), cyst 0.70 (0.66, 0.73), and no apparent disease 0.79 (0.75, 0.83). Conclusion: Weakly-supervised deep learning models were able to classify diverse diseases in multiple organ systems.

Related papers

• Robust Multicentre Detection and Classification of Colorectal Liver Metastases on CT: Application of Foundation Models [11.274035647041762]
  We developed a foundation model-based AI pipeline for patient-level classification and lesion-level detection of CRLM on CT.<n> UMedPT achieved the best performance and was fine-tuned with a head for classification and an FCOS-based head for lesion detection.
  arXiv  Detail & Related papers  (2026-01-12T14:35:29Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)
• BeyondCT: A deep learning model for predicting pulmonary function from chest CT scans [2.602923751641061]
  The BeyondCT model was developed to predict forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) from non-contrasted inspiratory chest CT scans. The model showed robust performance in predicting lung function from non-contrast inspiratory chest CT scans.
  arXiv  Detail & Related papers  (2024-08-10T22:28:02Z)
• Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification [12.25110399510034]
  This retrospective study included contrast-enhanced and non-contrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer. The model was trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age, 60 years +/- 11 [s.d.]; 143 female) Its performance was measured by the Dice coefficient, standard deviations, and 95% confidence intervals, focusing on ascites volume in the peritoneal cavity.
  arXiv  Detail & Related papers  (2024-06-23T01:32:53Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging [1.1567496318601842]
  We developed a high-performing model for kidney detection using a semi-supervised approach with a medical image library. Further external validation is required to assess the model's generalizability.
  arXiv  Detail & Related papers  (2024-02-08T16:54:20Z)
• Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice [0.0]
  We developed a deep-learning-based automatic detection algorithm (DLAD) to detect and localize seven specific radiological findings on chest X-rays. The proposed DLAD achieved high sensitivity (ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.905 (0.715-0.978), SCE 1.000 (0.366-1.000), CMG 0.837 (0.711-0.917), PNO 0.875 (0.538-0.986) The findings of the study demonstrate that the suggested DLAD holds potential for integration into everyday
  arXiv  Detail & Related papers  (2023-05-17T10:43:50Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Deep Learning for Segmentation-based Hepatic Steatosis Detection on Open Data: A Multicenter International Validation Study [5.117364766785943]
  This three-step AI workflow consists of 3D liver segmentation, liver attenuation measurements, and hepatic steatosis detection. The deep-learning segmentation achieved a mean coefficient of 0.957. If adopted for universal detection, this deep learning system could potentially allow early non-invasive, non-pharmacological preventative interventions.
  arXiv  Detail & Related papers  (2022-10-27T03:24:52Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)
• Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes [64.21642241351857]
  We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. We developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports. We also developed a model for multi-organ, multi-disease classification of chest CT volumes.
  arXiv  Detail & Related papers  (2020-02-12T00:59:23Z)

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.