Parameter Transfer Deep Neural Network for Single-Modal B-Mode Ultrasound-Based Computer-Aided Diagnosis

B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion

Learning using privileged information (LUPI) has shown its effectiveness to improve the B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) by transferring knowledge from the elasticity ultrasound (EUS). However, LUPI only performs transfer learning between the paired data with shared labels, and cannot handle the scenario of modality imbalance. In order to conduct the supervised transfer learning between the paired ultrasound data together with the additional single-modal BUS images, a novel multi-view LUPI algorithm with Dual-Level Modality Completion, named DLMC-LUPI, is proposed to improve the performance of BUS-based CAD. The DLMC-LUPI implements both image-level and feature-level (dual-level) completions of missing EUS modality, and then performs multi-view LUPI for knowledge transfer. Specifically, in the dual-level modality completion stage, a variational autoencoder (VAE) model for feature generation and a novel generative adversarial network (VAE-based GAN) model for image generation are sequentially trained. The proposed VAE-based GAN can improve the synthesis quality of EUS images by adopting the features generated by VAE from the BUS images as the model constrain to make the features generated from the synthesized EUS images more similar to them. In the multi-view LUPI stage, two feature vectors are generated from the real or pseudo images as two source domains, and then fed them to the multi-view support vector machine plus classifier for model training. The experiments on two ultrasound datasets indicate that the DLMC-LUPI outperforms all the compared algorithms, and it can effectively improve the performance of single-modal BUS-based CAD.

A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques

Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure for segmentation and classification with a large set of data modalities can be a challenging task. Therefore, a crucial requirement is to significantly develop the computer-assisted diagnostic system intended for the initial cancer identification. This article offers a systematic review of Deep Learning approaches using various image modalities to detect multi-organ cancers from 2012 to 2023. It emphasizes the detection of five supreme predominant tumors, i.e., breast, brain, lung, skin, and liver. Extensive review has been carried out by collecting research and conference articles and book chapters from reputed international databases, i.e., Springer Link, IEEE Xplore, Science Direct, PubMed, and Wiley that fulfill the criteria for quality evaluation. This systematic review summarizes the overview of convolutional neural network model architectures and datasets used for identifying and classifying the diverse categories of cancer. This study accomplishes an inclusive idea of ensemble deep learning models that have achieved better evaluation results for classifying the different images into cancer or healthy cases. This paper will provide a broad understanding to the research scientists within the domain of medical imaging procedures of which deep learning technique perform best over which type of dataset, extraction of features, different confrontations, and their anticipated solutions for the complex problems. Lastly, some challenges and issues which control the health emergency have been discussed.

Doubly Supervised Transfer Classifier for Computer-Aided Diagnosis With Imbalanced Modalities

Transfer learning (TL) can effectively improve diagnosis accuracy of single-modal-imaging-based computer-aided diagnosis (CAD) by transferring knowledge from other related imaging modalities, which offers a way to alleviate the small-sample-size problem. However, medical imaging data generally have the following characteristics for the TL-based CAD: 1) The source domain generally has limited data, which increases the difficulty to explore transferable information for the target domain; 2) Samples in both domains often have been labeled for training the CAD model, but the existing TL methods cannot make full use of label information to improve knowledge transfer. In this work, we propose a novel doubly supervised transfer classifier (DSTC) algorithm. In particular, DSTC integrates the support vector machine plus (SVM+) classifier and the low-rank representation (LRR) into a unified framework. The former makes full use of the shared labels to guide the knowledge transfer between the paired data, while the latter adopts the block-diagonal low-rank (BLR) to perform supervised TL between the unpaired data. Furthermore, we introduce the Schatten-p norm for BLR to obtain a tighter approximation to the rank function. The proposed DSTC algorithm is evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset and the bimodal breast ultrasound image (BBUI) dataset. The experimental results verify the effectiveness of the proposed DSTC algorithm.

Joint Localization and Classification of Breast Cancer in B-Mode Ultrasound Imaging via Collaborative Learning with Elastography

Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performing them simultaneously. Besides, they suffer from the limited diagnosis information in the B-mode ultrasound (BUS) images. In this study, we develop a novel network ResNet-GAP that incorporates both localization and classification into a unified procedure. To enhance the performance of ResNet-GAP, we leverage stiffness information in the elastography ultrasound (EUS) modality by collaborative learning in the training stage. Specifically, a dual-channel ResNet-GAP is developed, one channel for BUS and the other for EUS. In each channel, multiple class activity maps (CAMs) are generated using a series of convolutional kernels of different sizes. The multi-scale consistency of the CAMs in both channels are further considered in network optimization. Experiments on 264 patients in this study show that the newly developed ResNet-GAP achieves an accuracy of 88.6%, a sensitivity of 95.3%, a specificity of 84.6%, and an AUC of 93.6% on the classification task, and a 1.0NLF of 87.9% on the localization task, which is better than some state-of-the-art approaches.

Self-Supervised Bi-channel Transformer Networks for Computer-Aided Diagnosis

Self-supervised learning (SSL) can alleviate the issue of small sample size, which has shown its effectiveness for the computer-aided diagnosis (CAD) models. However, since the conventional SSL methods share the identical backbone in both the pretext and downstream tasks, the pretext network generally cannot be well trained in the pre-training stage, if the pretext task is totally different from the downstream one. In this work, we propose a novel task-driven SSL method, namely Self-Supervised Bi-channel Transformer Networks (SSBTN), to improve the diagnostic accuracy of a CAD model by enhancing SSL flexibility. In SSBTN, we innovatively integrate two different networks for the pretext and downstream tasks, respectively, into a unified framework. Consequently, the pretext task can be flexibly designed based on the data characteristics, and the corresponding designed pretext network thus learns more effective feature representation to be transferred to the downstream network. Furthermore, a transformer-based transfer module is developed to efficiently enhance knowledge transfer by conducting feature alignment between two different networks. The proposed SSBTN is evaluated on two publicly available datasets, namely the full-field digital mammography INbreast dataset and the wireless video capsule CrohnIPI dataset. The experimental results indicate that the proposed SSBTN outperforms all the compared algorithms.

Articles That Use Artificial Intelligence for Ultrasound: A Reader's Guide

Artificial intelligence (AI) transforms medical images into high-throughput mineable data. Machine learning algorithms, which can be designed for modeling for lesion detection, target segmentation, disease diagnosis, and prognosis prediction, have markedly promoted precision medicine for clinical decision support. There has been a dramatic increase in the number of articles, including articles on ultrasound with AI, published in only a few years. Given the unique properties of ultrasound that differentiate it from other imaging modalities, including real-time scanning, operator-dependence, and multi-modality, readers should pay additional attention to assessing studies that rely on ultrasound AI. This review offers the readers a targeted guide covering critical points that can be used to identify strong and underpowered ultrasound AI studies.

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Sentiment analysis aims to automatically classify the subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.