Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall

SIGNIFICANCE: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue. AIM: We aim to reduce the chest wall\u27s effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction. APPROACH: We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall. RESULTS: The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth. CONCLUSIONS: Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties

Difference imaging from single measurements in diffuse optical tomography: A deep learning approach

SIGNIFICANCE: Difference imaging, which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction. AIM: We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only. APPROACH: We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions. RESULTS: The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data. CONCLUSIONS: The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision

Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision transformer. However, existing VG methods tend to generate false-alarm objects when presented with inaccurate or irrelevant descriptions, which commonly occur in practical applications. Moreover, existing methods fail to capture fine-grained features, accurate localization, and sufficient context comprehension from the whole image and textual descriptions. To address both issues, we propose an Iterative Robust Visual Grounding (IR-VG) framework with Masked Reference based Centerpoint Supervision (MRCS). The framework introduces iterative multi-level vision-language fusion (IMVF) for better alignment. We use MRCS to ahieve more accurate localization with point-wised feature supervision. Then, to improve the robustness of VG, we also present a multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of false-alarm objects when presented with inaccurate expressions. The proposed framework is evaluated on five regular VG datasets and two newly constructed robust VG datasets. Extensive experiments demonstrate that IR-VG achieves new state-of-the-art (SOTA) results, with improvements of 25\% and 10\% compared to existing SOTA approaches on the two newly proposed robust VG datasets. Moreover, the proposed framework is also verified effective on five regular VG datasets. Codes and models will be publicly at https://github.com/cv516Buaa/IR-VG

Polysulfide Catalytic Materials for Fast-Kinetic Metal–Sulfur Batteries: Principles and Active Centers

Benefiting from the merits of low cost, ultrahigh-energy densities, and environmentally friendliness, metal–sulfur batteries (M–S batteries) have drawn massive attention recently. However, their practical utilization is impeded by the shuttle effect and slow redox process of polysulfide. To solve these problems, enormous creative approaches have been employed to engineer new electrocatalytic materials to relieve the shuttle effect and promote the catalytic kinetics of polysulfides. In this review, recent advances on designing principles and active centers for polysulfide catalytic materials are systematically summarized. At first, the currently reported chemistries and mechanisms for the catalytic conversion of polysulfides are presented in detail. Subsequently, the rational design of polysulfide catalytic materials from catalytic polymers and frameworks to active sites loaded carbons for polysulfide catalysis to accelerate the reaction kinetics is comprehensively discussed. Current breakthroughs are highlighted and directions to guide future primary challenges, perspectives, and innovations are identified. Computational methods serve an ever-increasing part in pushing forward the active center design. In summary, a cutting-edge understanding to engineer different polysulfide catalysts is provided, and both experimental and theoretical guidance for optimizing future M–S batteries and many related battery systems are offered