RES: A Robust Framework for Guiding Visual Explanation

Gao, Yuyang; Sun, Tanfeng; Guangji, Bai,; Gu, Siyi; Ray, Hong, Sungsoo; Zhao, Liang

doi:10.1145/3534678.3539419

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…A natural idea is to exploit these nodule contour annotations to improve the training effect. This can be achieved by using explanation supervision technique, which incorporates explanation annotations (i.e., these nodule contour annotations) into supervision signal, improving both classification performance and inference attribution 34 . However, to our knowledge, no one has applied explanation supervision to clinical diagnosis, leaving the wealth of information in explanation annotations untapped.…”

Section: Methodsmentioning

confidence: 99%

“…This can be achieved by using explanation supervision technique, which incorporates explanation annotations (i.e., these nodule contour annotations) into supervision signal, improving both classification performance and inference attribution. 34 However, to our knowledge, no one has applied explanation supervision to clinical diagnosis, leaving the wealth of information in explanation annotations untapped. RES is a framework that utilizes explanation annotations to improve both the performance and the explanation quality of backbone DNN.…”

Section: Res For Pulmonary Nodule Detectionmentioning

confidence: 99%

“…RES (Robust Explanation Supervision) 34 is a generic framework for guiding visual explanation that allows DL models to be trained using even noisy annotations. For example, inaccurate nodule contours will be imputed based on a prior about the inaccuracy.…”

Section: Introductionmentioning

confidence: 99%

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Robust explanation supervision for false positive reduction in pulmonary nodule detection

Zhao,

Chang,

Yang

et al. 2024

Medical Physics

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BackgroundLung cancer is the deadliest and second most common cancer in the United States due to the lack of symptoms for early diagnosis. Pulmonary nodules are small abnormal regions that can be potentially correlated to the occurrence of lung cancer. Early detection of these nodules is critical because it can significantly improve the patient's survival rates. Thoracic thin‐sliced computed tomography (CT) scanning has emerged as a widely used method for diagnosing and prognosis lung abnormalities.PurposeThe standard clinical workflow of detecting pulmonary nodules relies on radiologists to analyze CT images to assess the risk factors of cancerous nodules. However, this approach can be error‐prone due to the various nodule formation causes, such as pollutants and infections. Deep learning (DL) algorithms have recently demonstrated remarkable success in medical image classification and segmentation. As an ever more important assistant to radiologists in nodule detection, it is imperative ensure the DL algorithm and radiologist to better understand the decisions from each other. This study aims to develop a framework integrating explainable AI methods to achieve accurate pulmonary nodule detection.MethodsA robust and explainable detection (RXD) framework is proposed, focusing on reducing false positives in pulmonary nodule detection. Its implementation is based on an explanation supervision method, which uses nodule contours of radiologists as supervision signals to force the model to learn nodule morphologies, enabling improved learning ability on small dataset, and enable small dataset learning ability. In addition, two imputation methods are applied to the nodule region annotations to reduce the noise within human annotations and allow the model to have robust attributions that meet human expectations. The 480, 265, and 265 CT image sets from the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC‐IDRI) dataset are used for training, validation, and testing.ResultsUsing only 10, 30, 50, and 100 training samples sequentially, our method constantly improves the classification performance and explanation quality of baseline in terms of Area Under the Curve (AUC) and Intersection over Union (IoU). In particular, our framework with a learnable imputation kernel improves IoU from baseline by 24.0% to 80.0%. A pre‐defined Gaussian imputation kernel achieves an even greater improvement, from 38.4% to 118.8% from baseline. Compared to the baseline trained on 100 samples, our method shows less drop in AUC when trained on fewer samples. A comprehensive comparison of interpretability shows that our method aligns better with expert opinions.ConclusionsA pulmonary nodule detection framework was demonstrated using public thoracic CT image datasets. The framework integrates the robust explanation supervision (RES) technique to ensure the performance of nodule classification and morphology. The method can reduce the workload of radiologists and enable them to focus on the diagnosis and prognosis of the potential cancerous pulmonary nodules at the early stage to improve the outcomes for lung cancer patients.

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Section: Methodsmentioning

confidence: 99%

Section: Res For Pulmonary Nodule Detectionmentioning

confidence: 99%

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Robust explanation supervision for false positive reduction in pulmonary nodule detection

Zhao,

Chang,

Yang

et al. 2024

Medical Physics

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“…(2) Explainable AI Framework Illustrated in Figure 1 is the architecture of the proposed explainable lung nodule detection (ELND) framework, leveraging the visual explanation approach encapsulated in the previous study [29]. The framework integrates a convolutional neural network (CNN) model, ResNet18 [30], that can be trained to utilize the explanation loss function, incorporating a Gaussian kernel function.…”

Section: Methods (1) Patient Data and Data Preprocessingmentioning

confidence: 99%

Explainable AI for lung nodule detection and classification in CT images

Chang,

Zhao,

Zhao

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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Lung cancer is the second most prevalent and deadliest cancer in the United States, primarily due to its elusive early symptoms that hinder timely diagnosis. Lung nodules, minute anomalous areas, hold potential significance in lung cancer occurrence. Swift identification of these nodules can significantly enhance patient survival rates. Thoracic thin-sliced Computed Tomography (CT) scanning has emerged as a widely adopted approach for radiologists' diagnosing and prognosticating lung abnormalities. However, human factors can be prone to errors stemming from the multifarious causes underlying nodule formation, including factors like pollutants and infections. The domain of deep learning algorithms has recently showcased remarkable prowess in classifying and segmenting medical images. This study is geared towards the creation of a comprehensive framework that seamlessly integrates explainable AI techniques to achieve precise pulmonary nodule detection for computer-aided detection and diagnosis (CAD) systems. The framework's execution is underpinned by an explanation supervision approach, employing radiologists' nodule contours as guiding signals to imbue the model with an understanding of nodule morphologies. The nodule detection is achieved by augmentation of the model's learning capabilities, particularly on smaller datasets. Furthermore, two imputation techniques are deployed to refine the nodule region annotations, thereby attenuating noise inherent in human annotations. These techniques empower the model with steadfast attributions that align with human expectations. Our approach consistently elevates ResNet18's classification performance and explanation quality regarding accuracy and specificity by 22% and 23% using training datasets with 100 samples. For small dataset learning using 50 samples, the proposed framework still outperforms ResNet18 by 16% regarding accuracy and specificity. This framework seamlessly integrates a robust explanation supervision technique, guaranteeing nodule classification and morphology assessment accuracy. Reducing the burden on radiologists allows them to focus on diagnosing and prognosticating potentially cancerous pulmonary nodules at an early stage, consequently augmenting survival rates in lung cancer.

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“…Saliency detection is to identify the most important and informative part of input features. It has been applied to various domains including CV [13,16], NLP [23,32], etc. The salience map approach is exemplified by [45] to test a network with portions of the input occluded to create a map showing which parts of the data actually have an influence on the network output.…”

Section: Related Workmentioning

confidence: 99%

Saliency-Regularized Deep Multi-Task Learning

Guangji¹,

Zhao²

2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Multi-task learning (MTL) is a framework that enforces multiple learning tasks to share their knowledge to improve their generalization abilities. While shallow multi-task learning can learn task relations, it can only handle pre-defined features. Modern deep multi-task learning can jointly learn latent features and task sharing, but they are obscure in task relation. Also, they pre-define which layers and neurons should share across tasks and cannot learn adaptively. To address these challenges, this paper proposes a new multi-task learning framework that jointly learns latent features and explicit task relations by complementing the strength of existing shallow and deep multitask learning scenarios. Specifically, we propose to model the task relation as the similarity between tasks' input gradients, with a theoretical analysis of their equivalency. In addition, we innovatively propose a multi-task learning objective that explicitly learns task relations by a new regularizer. Theoretical analysis shows that the generalizability error has been reduced thanks to the proposed regularizer. Extensive experiments on several multi-task learning and image classification benchmarks demonstrate the proposed method's effectiveness, efficiency as well as reasonableness in the learned task relation patterns.

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RES: A Robust Framework for Guiding Visual Explanation

Cited by 15 publications

References 19 publications

Robust explanation supervision for false positive reduction in pulmonary nodule detection

Robust explanation supervision for false positive reduction in pulmonary nodule detection

Explainable AI for lung nodule detection and classification in CT images

Saliency-Regularized Deep Multi-Task Learning

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