Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

Abstract

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Deep Learning for Medical Image Analysis: Applications to Computed Tomography and Magnetic Resonance Imaging

“…The most basic approach is to fine tune with a small set of labeled data after extracting relevant features from unlabeled data via unsupervised learning. Cheng et al obtained a performance better than the current CADx system by pretraining a stacked denoising autoencoder (SDAE) to determine the malignancy of a lesion from chest CT images [13].…”

Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies

“…Furthermore, we plan to use a deep learning approach to find discriminant features and combine them with handcrafted features (e.g., local patterns from LQP operators). Although several studies [22][23][24][25][26][27][28][29] have investigated the use of deep learning in breast density classification, to the best of our knowledge, the separation was only based on two-or three-class classification (none of the deep learning based approaches has been applied to four-class classification). In fact, no study has been conducted combining non-handcrafted and handcrafted features in breast density classification.…”

“…Qui et al [27] proposed three pairs of convolution-max-pooling layers that contain 20, 10, and five feature maps to predict short-term breast cancer risk and achieved 71.4%. Using a deeper network approach, Cheng et al [28] proposed an eight multi-layer deep learning algorithm to employ three pairs of convolution-max-pooling layers for mammographic masses classification and reported AUC of 0.81. In a similar problem domain (mass classification), Jiao et al [29] used CNN and a decision mechanism along with intensity information and reported an average of 95% accuracy on different sizes of datasets.…”

Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods (Preprint)

“…For instance, when building a disease diagnosis model, patient's various information such as symptoms, tests as well as diagnosis should be extracted and fed into the model [14,15]. However, Formal studies usually focused on recognizing a single type of clinical entities from clinical texts.…”