Colonoscopy is considered the gold-standard investigation for colorectal cancer screening. However, the polyps miss rate in clinical practice is relatively high due to different factors. This presents an opportunity to use AI models to automatically detect and segment polyps, supporting clinicians to reduce the number of polyps missed. Inspired by the success of UNets, a popular strategy for solving medical image segmentation tasks, this article proposes a novel framework for polyp segmentation called CRF-EfficientUNet, which enhances UNet using the EfficientNet encoder, a combined asymmetric loss function, and Conditional Random Field as a Recurrent Neural Network (CRF-RNN) layer on top. A novel loss function that combines pixel-wise cross-entropy loss and asymmetric similarity loss to solve the unbalanced imaging data problem is proposed. Training the proposed network with this loss function can achieve a considerably higher Dice score and better polyp segmentation prediction. In addition, we add the CRF-RNN layer to the proposed framework to improve the quality of semantic segmentation. Experimental results on popular benchmark datasets show that CRF-EfficientUNet achieves state-of-the-art accuracy compared to existing methods. The results of the experiments, which are performed on the CVC-ClinicDB dataset for training and testing, are 95.55% Dice and 92.23% IoU. While the experimental results on cross-dataset using Kvasir-SEG as the training set, CVC-ColonDB as the test set are 85.59% Dice and 76.19% IoU. These results indicate that the proposed method has high generalization capability and learning ability, and it can be a compelling choice for practical applications with considerable data variations.

Background: Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements. However, there has been no examination of how segmentation accuracy affects MI classification performance or the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning.Materials and Methods: Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity.Results: The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%.Conclusions: Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we adapt U-net and evaluate its performance with different modern convolutional neural networks as its encoder for polyp segmentation. One of the major challenges in training networks for polyp segmentation raises when the data are unbalanced, polyp pixels are often much lower in numbers than non-polyp pixels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased toward the non-polyp class which is particularly undesired because false negatives are more important than false positives. We propose an asymmetric similarity loss function to address this problem and achieve a much better tradeoff between precision and recall. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance of well-known polyp datasets CVC-ColonDB and ETIS-Larib PolypDB. The best results are 89.13% dice, 79.77% IOU, 90.15% recall, and 86.28% precision. Our proposed method outperforms the state-of-the-art polyp segmentation methods.

Much of the earth’s surface is covered by water. As was pointed out in the 2020 edition of the World Water Development Report, climate change challenges the sustainability of global water resources, so it is important to monitor the quality of water to preserve sustainable water resources. Quality of water can be related to the structure of water crystal, the solid-state of water, so methods to understand water crystals can help to improve water quality. As a first step, a water crystal exploratory analysis has been initiated with the cooperation with the Emoto Peace Project (EPP). The 5K EPP dataset has been created as the first world-wide small dataset of water crystals. Our research focused on reducing the inherent limitations when fitting machine learning models to the 5K EPP dataset. One major result is the classification of water crystals and how to split our small dataset into several related groups. Using the 5K EPP dataset of human observations and past research on snow crystal classification, we created a simple set of visual labels to identify water crystal shapes, in 13 categories. A deep learning-based method has been used to automatically do the classification task with a subset of the label dataset. The classification achieved high accuracy when using a fine-tuning technique.