Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation

The Koos grading scale is a classification system for vestibular schwannoma (VS) used to characterize the tumor and its effects on adjacent brain structures. The Koos classification captures many of the characteristics of treatment deci-sions and is often used to determine treatment plans. Although both contrast-enhanced T1 (ceT1) scanning and high-resolution T2 (hrT2) scanning can be used for Koos Classification, hrT2 scanning is gaining interest because of its higher safety and cost-effectiveness. However, in the absence of annotations for hrT2 scans, deep learning methods often inevitably suffer from performance deg-radation due to unsupervised learning. If ceT1 scans and their annotations can be used for unsupervised learning of hrT2 scans, the performance of Koos classifi-cation using unlabeled hrT2 scans will be greatly improved. In this regard, we propose an unsupervised cross-modality domain adaptation method based on im-age translation by transforming annotated ceT1 scans into hrT2 modality and us-ing their annotations to achieve supervised learning of hrT2 modality. Then, the VS and 7 adjacent brain structures related to Koos classification in hrT2 scans were segmented. Finally, handcrafted features are extracted from the segmenta-tion results, and Koos grade is classified using a random forest classifier. The proposed method received rank 1 on the Koos classification task of the Cross-Modality Domain Adaptation (crossMoDA 2022) challenge, with Macro-Averaged Mean Absolute Error (MA-MAE) of 0.2148 for the validation set and 0.26 for the test set.Comment: 10 pages, 2 figure

A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

In this letter, we propose a novel offloading learning approach to compromise energy consumption and latency in a multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach

Mechanisms of the interaction between Pr(DNR)3 and Herring-Sperm DNA

Research on the interaction mechanism of drugs with DNA is essential to understand their pharmacokinetics. The interaction between rare earth complexes Pr(DNR)3 and Herring-Sperm DNA was studied in Tris-HCl buffer solution (pH 7.4) by absorption and fluorescence spectroscopy and viscosity measurements. The results showed that the modes of interaction between Pr(DNR)3 and Herring-Sperm DNA were electrostatic and intercalation. The binding ratio was nPr(DNA)3 ׃ nDNA = 5׃1 and the binding constant was KΘ292K = 4.34×10exp3 L mol-1. Furthermore, according to the double reciprocal method and the thermodynamic equation, the intercalative interaction was cooperatively driven by an enthalpy effect and an entropy effect

A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment

Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance. Automatic segmentation of kidney, renal tumor, renal vein and renal artery benefits a lot on surgery-based renal cancer treatment. In this paper, we propose a new nnhra-unet network, and use a multi-stage framework which is based on it to segment the multi-structure of kidney and participate in the KiPA2022 challenge

Intergrated Segmentation and Detection Models for Dentex Challenge 2023

Dental panoramic x-rays are commonly used in dental diagnosing. With the development of deep learning, auto detection of diseases from dental panoramic x-rays can help dentists to diagnose diseases more efficiently.The Dentex Challenge 2023 is a competition for automatic detection of abnormal teeth along with their enumeration ids from dental panoramic x-rays. In this paper, we propose a method integrating segmentation and detection models to detect abnormal teeth as well as obtain their enumeration ids.Our codes are available at https://github.com/xyzlancehe/DentexSegAndDet