Deep Clustering Activation Maps for Emphysema Subtyping
- URL: http://arxiv.org/abs/2106.01351v1
- Date: Tue, 1 Jun 2021 11:24:48 GMT
- Title: Deep Clustering Activation Maps for Emphysema Subtyping
- Authors: Weiyi Xie, Colin Jacobs, Bram van Ginneken
- Abstract summary: We propose a deep learning clustering method that exploits dense features from a segmentation network for emphysema subtyping from computed tomography (CT) scans.
Using dense features enables high-resolution visualization of image regions corresponding to the cluster assignment via dense clustering activation maps (dCAMs)
We evaluated clustering results on 500 subjects from the COPDGenestudy, where radiologists manually annotated emphysema sub-types according to their visual CT assessment.
- Score: 9.313053265087262
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a deep learning clustering method that exploits dense features
from a segmentation network for emphysema subtyping from computed tomography
(CT) scans. Using dense features enables high-resolution visualization of image
regions corresponding to the cluster assignment via dense clustering activation
maps (dCAMs). This approach provides model interpretability. We evaluated
clustering results on 500 subjects from the COPDGenestudy, where radiologists
manually annotated emphysema sub-types according to their visual CT assessment.
We achieved a 43% unsupervised clustering accuracy, outperforming our baseline
at 41% and yielding results comparable to supervised classification at 45%. The
proposed method also offers a better cluster formation than the baseline,
achieving0.54 in silhouette coefficient and 0.55 in David-Bouldin scores.
Related papers
- Automated dermatoscopic pattern discovery by clustering neural network
output for human-computer interaction [0.39462888523270856]
The objective of this study was to create an automated clustering resulting in human-interpretable pattern discovery.
Images from the public HAM10000 dataset, including 7 common pigmented skin lesion diagnoses, were tiled into 29420 tiles and clustered via k-means.
arXiv Detail & Related papers (2023-09-15T16:50:47Z) - Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep
Neural Networks [5.322495071033588]
We present a deep learning-based approach for automating the Fleischner Society's visual score system for emphysema subtyping and severity analysis.
Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method's accuracy of 45%.
The proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema.
arXiv Detail & Related papers (2023-09-05T20:54:41Z) - Superresolution and Segmentation of OCT scans using Multi-Stage
adversarial Guided Attention Training [18.056525121226862]
We propose the multi-stage & multi-discriminatory generative adversarial network (MultiSDGAN) to translate OCT scans in high-resolution segmentation labels.
We evaluate and compare various combinations of channel and spatial attention to the MultiSDGAN architecture to extract more powerful feature maps.
Our results demonstrate relative improvements of 21.44% and 19.45% on the Dice coefficient and SSIM, respectively.
arXiv Detail & Related papers (2022-06-10T00:26:55Z) - An Efficient End-to-End Deep Neural Network for Interstitial Lung
Disease Recognition and Classification [0.5424799109837065]
This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns.
The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function.
A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model.
arXiv Detail & Related papers (2022-04-21T06:36:10Z) - 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) - Multi-Scale Input Strategies for Medulloblastoma Tumor Classification
using Deep Transfer Learning [59.30734371401316]
Medulloblastoma is the most common malignant brain cancer among children.
CNN has shown promising results for MB subtype classification.
We study the impact of tile size and input strategy.
arXiv Detail & Related papers (2021-09-14T09:42:37Z) - Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of
Heart Failure Patients [50.48904066814385]
In this work we apply deep semi-supervised embedded clustering to determine data-driven patient subgroups of heart failure.
We find clinically relevant clusters from an embedded space derived from heterogeneous data.
The proposed algorithm can potentially find new undiagnosed subgroups of patients that have different outcomes.
arXiv Detail & Related papers (2020-12-24T12:56:46Z) - Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
Existing scalable hierarchical clustering methods sacrifice quality for speed.
We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
arXiv Detail & Related papers (2020-10-22T15:58:35Z) - Multilabel 12-Lead Electrocardiogram Classification Using Gradient
Boosting Tree Ensemble [64.29529357862955]
We build an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis.
For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform.
We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class.
arXiv Detail & Related papers (2020-10-21T18:11:36Z) - Cluster Activation Mapping with Applications to Medical Imaging [4.98888193036705]
We developed methodology to generate CLuster Activation Mapping (CLAM)
We applied it to 3D CT scans from a sarcoidosis population to identify new clusters of sarcoidosis based purely on CT scan presentation.
arXiv Detail & Related papers (2020-10-09T20:37:09Z) - Adaptive Feature Selection Guided Deep Forest for COVID-19
Classification with Chest CT [49.09507792800059]
We propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images.
We evaluate our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP)
arXiv Detail & Related papers (2020-05-07T06:00:02Z)
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.