Anh Dinh

Mohammad Reza Mohebbian, Khan A. Wahid, Anh Dinh et al.

Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are known tools for diagnosing gastrointestinal (GI) tract disorders. Detecting the anatomical location of GI tract can help clinicians to determine a more appropriate treatment plan, can reduce repetitive endoscopy and is important in drug-delivery. There are few research that address detecting anatomical location of WCE and CE images using classification, mainly because of difficulty in collecting data and anotating them. In this study, we present a few-shot learning method based on distance metric learning which combines transfer-learning and manifold mixup scheme for localizing endoscopy frames and can be trained on few samples. The manifold mixup process improves few-shot learning by increasing the number of training epochs while reducing overfitting, as well as providing more accurate decision boundaries. A dataset is collected from 10 different anatomical positions of human GI tract. Two models were trained using only 78 CE and 27 WCE annotated frames to predict the location of 25700 and 1825 video frames from CE and WCE, respectively. In addition, we performed subjective evaluation using nine gastroenterologists to show the necessaity of having an AI system for localization. Various ablation studies and interpretations are performed to show the importance of each step, such effect of transfer-learning approach, and impact of manifold mixup on performance. The proposed method is also compared with various methods trained on categorical cross-entropy loss and produced better results which show that proposed method has potential to be used for endoscopy image classification.

Mohammad Reza Mohebbian, Seyed Shahim Vedaei, Khan A. Wahid et al.

Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal. to map MECG to FECG efficiently. The high correlation between maternal and fetal ECG parts decreases the performance of convolution layers. Therefore, the masking region of interest using the attention mechanism is performed for improving signal generators' precision. The sine activation function is also used since it could retain more details when converting two signal domains. Three available datasets from the Physionet, including A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, are used to evaluate the performance. The proposed method could map abdominal MECG to scalp FECG with an average 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on, A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. These results are comparable to the state-of-the-art; thus, the proposed algorithm has the potential of being used for high-performance signal-to-signal conversion.