An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification (1806.00712v1)

Abstract: While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches.

• The paper introduces an Interpretable Deep Hierarchical Semantic Convolutional Neural Network (HSCNN) for classifying lung nodule malignancy, addressing the black-box problem in deep learning by integrating radiologist-interpretable semantic features.
• The HSCNN model provides two-tiered outputs: low-level semantic features (like margin texture, sphericity) and high-level malignancy scores, trained jointly to improve both interpretability and prediction accuracy.
• Evaluated on the LIDC dataset, the model achieves superior performance with an AUC of 0.856 and improved sensitivity, demonstrating that integrating semantic features enhances interpretability and aids potential clinical adoption by radiologists.

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

The paper under review introduces a novel method for classifying lung nodules as benign or malignant using a hierarchical semantic convolutional neural network (HSCNN). This method addresses the prevalent challenge in deep learning models—namely, the lack of interpretability, a crucial barrier to clinical implementation in computer-aided diagnosis systems. By integrating domain knowledge through the use of semantic features, this approach attempts to provide a more transparent prediction process that aligns with the interpretive processes employed by radiologists.

Technical Approach

The HSCNN model offers a two-tiered prediction output: low-level radiologist semantic features and high-level malignancy scores. The low-level semantic features, which include nodule properties like margin texture, sphericity, subtlety, calcification, etc., are derived qualitatively yet can be quantified using deep CNN models. These features are interpretable and intelligible to radiologists, offering an explanatory view of the network’s decision-making process. The architecture is designed with a primary feature learning module that feeds into separate low-level task modules and a high-level task module, which ultimately predicts nodule malignancy. The model is trained using a global loss function that combines both the low- and high-level tasks, optimizing the ability to predict each task simultaneously within a joint framework.

Results

The model is evaluated using the Lung Image Database Consortium (LIDC), demonstrating superior performance as compared to traditional 3D CNN architectures. Specifically, the integration of semantic features not only aids model interpretability but also improves prediction accuracy, achieving an AUC of 0.856. The system offers a substantive improvement in model sensitivity without compromising specificity, achieving a statistically significant enhancement in overall performance. The interpretability is visually validated by demonstrating accurate predictions of malignancy alongside the corresponding semantic features.

Implications

The interpretability of such a network holds significant implications for clinical implementation, facilitating better acceptance by domain experts such as radiologists. By providing insight into the decision-making process of the neural network, a radiologist can calibrate their trust in the machine’s predictions, crucial for clinical adoption. Furthermore, the framework detailed in this paper could serve as a blueprint for designing interpretable models in other realms of medical diagnostics, where the black-box nature of deep learning approaches is a persistent concern.

Future Directions

Future research could explore the incorporation of additional semantic features more strongly correlated with malignancy, such as spiculation and lobulation. Moreover, the consequences of binarizing semantic labels might be minimized by employing a multi-class framework that better reflects the radiological nuances captured in practice. Additionally, as suggested by the discussed limitations, training on larger, more consistently annotated datasets could further improve both the accuracy and robustness of semantic feature predictions and malignancy classification. The expansion and adaptation of this methodology to larger and more diverse datasets could potentially enhance performance and generalizability, strengthening its applicability across varied clinical scenarios.

In conclusion, this paper offers a significant contribution to the evolving field of interpretable AI in medical diagnostics, presenting an overview of deep learning capabilities with domain-specific knowledge to enable trust and insight in automatic decision-making systems.