Tao Gan

Bio: Tao Gan is an academic researcher from Sichuan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 7, co-authored 29 publications receiving 170 citations.

Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images

Abstract: Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks (CNNs) have made achievements in GI endoscopy image analysis. This review focuses on the applications of DL methods in the analysis of GI images. We summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks. At the end, we give a discussion on the challenges and the research directions of GI image analysis based on DL in the future.

Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network.

Abstract: The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.

Efficacy and safety of endoscopic submucosal tunnel dissection for superficial esophageal squamous cell carcinoma and precancerous lesions.

Abstract: Efficacy and safety of endoscopic submucosal tunnel dissection for superficial esophageal squamous cell carcinoma and precancerous lesions

Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging.

Abstract: Magnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI. A total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts. Our CNN system showed high accuracy, sensitivity and specificity in the diagnosis of early gastric cancer. It is anticipated that more progress will be made in optimization of the CNN diagnostic system and further development of artificial intelligence in the medical field.

Herbal traditional Chinese medicine and its evidence base in gastrointestinal disorders.

Abstract: Herbal traditional Chinese medicine (TCM) is used to treat several ailments, but its efficiency is poorly documented and hence debated, as opposed to modern medicine commonly providing effective therapies. The aim of this review article is to present a practical reference guide on the role of herbal TCM in managing gastrointestinal disorders, supported by systematic reviews and evidence based trials. A literature search using herbal TCM combined with terms for gastrointestinal disorders in PubMed and the Cochrane database identified publications of herbal TCM trials. Results were analyzed for study type, inclusion criteria, and outcome parameters. Quality of placebo controlled, randomized, double-blind clinical trials was poor, mostly neglecting stringent evidence based diagnostic and therapeutic criteria. Accordingly, appropriate Cochrane reviews and meta-analyses were limited and failed to support valid, clinically relevant evidence based efficiency of herbal TCM in gastrointestinal diseases, including gastroesophageal reflux disease, gastric or duodenal ulcer, dyspepsia, irritable bowel syndrome, ulcerative colitis, and Crohn's disease. In conclusion, the use of herbal TCM to treat various diseases has an interesting philosophical background with a long history, but it received increasing skepticism due to the lack of evidence based efficiency as shown by high quality trials; this has now been summarized for gastrointestinal disorders, with TCM not recommended for most gastrointestinal diseases. Future studies should focus on placebo controlled, randomized, double-blind clinical trials, herbal product quality and standard criteria for diagnosis, treatment, outcome, and assessment of adverse herb reactions. This approach will provide figures of risk/benefit profiles that hopefully are positive for at least some treatment modalities of herbal TCM. Proponents of modern herbal TCM best face these promising challenges of pragmatic modern medicine by bridging the gap between the two medicinal cultures.

Deep Learning for Medical Anomaly Detection – A Survey

Abstract: Machine learning–based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.