Interpretable Gender Classification from Retinal Fundus Images Using BagNets

Ilanchezian, Indu; Kobak, Dmitry; Faber, Hanna; Ziemssen, Focke; Berens, Philipp; Ayhan, Murat Seçkin

doi:10.1007/978-3-030-87199-4_45

Cited by 17 publications

(18 citation statements)

References 23 publications

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Section: Introductionmentioning

confidence: 99%

“…Convolutional neural networks (CNNs) with restricted receptive fields have been applied to relate the feature space and the pixel space exactly ( 16 – 18 ). In these models, the images are sliced into patches and the features are extracted within local patches ( 16 – 18 ). The class evidences produced by local features are averaged across all patches to infer the image-level labels with the softmax activation ( 16 – 18 ).…”

Section: Introductionmentioning

confidence: 99%

“…In these models, the images are sliced into patches and the features are extracted within local patches ( 16 – 18 ). The class evidences produced by local features are averaged across all patches to infer the image-level labels with the softmax activation ( 16 – 18 ). However, the selection of the patch size is a hard problem for CNNs with restricted receptive fields to apply in weakly supervised segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…However, the selection of the patch size is a hard problem for CNNs with restricted receptive fields to apply in weakly supervised segmentation. Increasing the patch size expands the receptive field and leads to better local features for classification, but coarsens the segmentation output ( 16 – 18 ). Another problem is the way to aggregate pixel-level evidences to the image-level decision.…”

Section: Introductionmentioning

confidence: 99%

“…Another problem is the way to aggregate pixel-level evidences to the image-level decision. Unlike the images used in ( 16 – 18 ), all of which contact objects, the medical image datasets contain an extra class: no lesion. Averaging the class evidences, patches have the same weight to infer the image-level class.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Chest L-Transformer: Local Features With Position Attention for Weakly Supervised Chest Radiograph Segmentation and Classification

Wang

Qin

et al. 2022

Front. Med.

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We consider the problem of weakly supervised segmentation on chest radiographs. The chest radiograph is the most common means of screening and diagnosing thoracic diseases. Weakly supervised deep learning models have gained increasing popularity in medical image segmentation. However, these models are not suitable for the critical characteristics presented in chest radiographs: the global symmetry of chest radiographs and dependencies between lesions and their positions. These models extract global features from the whole image to make the image-level decision. The global symmetry can lead these models to misclassification of symmetrical positions of the lesions. Thoracic diseases often have special disease prone areas in chest radiographs. There is a relationship between the lesions and their positions. In this study, we propose a weakly supervised model, called Chest L-Transformer, to take these characteristics into account. Chest L-Transformer classifies an image based on local features to avoid the misclassification caused by the global symmetry. Moreover, associated with Transformer attention mechanism, Chest L-Transformer models the dependencies between the lesions and their positions and pays more attention to the disease prone areas. Chest L-Transformer is only trained with image-level annotations for lesion segmentation. Thus, Log-Sum-Exp voting and its variant are proposed to unify the pixel-level prediction with the image-level prediction. We demonstrate a significant segmentation performance improvement over the current state-of-the-art while achieving competitive classification performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Chest L-Transformer: Local Features With Position Attention for Weakly Supervised Chest Radiograph Segmentation and Classification

Wang

Qin

et al. 2022

Front. Med.

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Estimating lung function from computed tomography at the patient and lobe level using machine learning

Boulogne,

Charbonnier,

Jacobs

et al. 2024

Medical Physics

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BackgroundAutomated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction.PurposeTo automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient's lung function.MethodsWe propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient's total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe‐level and a patient‐level model. The lobe‐level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient‐level model directly estimates patient level lung function from a CT scan and is used to re‐scale the output of the lobe‐level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8 433 CT volumes for training, 1 775 CT volumes for validation, and 1 873 CT volumes for testing.ResultsFirst, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe‐level quantities, such as COVID‐19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient‐level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono‐oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377 L, 0.297 L, and 2.800 mL/min/mm Hg respectively. We release the resulting algorithms for lung function estimation to the research community at https://grand‐challenge.org/algorithms/lobe‐wise‐lung‐function‐estimation/ConclusionsI3Dr can estimate global measures from an image, as well as the contributions of individual parts of the image to this global measure. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient's lung function. The findings presented in this work may advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases as well as in risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction.

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Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images

Boreiko

Ilanchezian

Ayhan

et al. 2022

Lecture Notes in Computer Science

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Interpretable Gender Classification from Retinal Fundus Images Using BagNets

Cited by 17 publications

References 23 publications

Chest L-Transformer: Local Features With Position Attention for Weakly Supervised Chest Radiograph Segmentation and Classification

Chest L-Transformer: Local Features With Position Attention for Weakly Supervised Chest Radiograph Segmentation and Classification

Estimating lung function from computed tomography at the patient and lobe level using machine learning

Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images

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