Cataract is one of the leading causes of reversible visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art artificial intelligence techniques for automatic cataract classification and grading, helping clinicians prevent and treat cataract in time. This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning techniques and deep learning techniques. This paper also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification and grading based on machine learning techniques and present possible solutions to these challenges for future research.

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey.

CNN and transformer framework for insect pest classification

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A Survey on Deep Learning for Skin Lesion Segmentation

3D Medical image segmentation using parallel transformers

—Convolutional neural networks (CNN), the most prevailing architecture for deep-learning based medical image analysis, are still functionally limited by their intrinsic inductive biases and inadequate receptive ﬁelds. Transformer, born to address this issue, has drawn explosive attention in natural language processing and computer vision due to its remarkable ability in capturing long-range dependency. However, most re- cent transformer-based methods for medical image segmentation directly apply vanilla transformers as an auxiliary module in CNN-based methods, resulting in severe detail loss due to the rigid patch partitioning scheme in transformers. To address this problem, we propose C2FTrans, a novel multi-scale architecture that formulates medical image segmentation as a coarse-to-ﬁne procedure. C2FTrans mainly consists of a cross-scale global transformer (CGT) which addresses local contextual similarity in CNN and a boundary-aware local transformer (BLT) which over-comes boundary uncertainty brought by rigid patch partitioning in transformers. Speciﬁcally, CGT builds global dependency across three different small-scale feature maps to obtain rich global semantic features with an acceptable computational cost, while BLT captures mid-range dependency by adaptively generat- ing windows around boundaries under the guidance of entropy to reduce computational complexity and minimize detail loss based on large-scale feature maps. Extensive experimental results on three public datasets demonstrate the superior performance of C2FTrans against state-of-the-art CNN-based and transformer-based methods with fewer parameters and lower FLOPs. We believe the design of C2FTrans would further inspire future work on developing efﬁcient and lightweight transformers for medical image segmentation. The source code of this paper is publicly available at https://github.com/xianlin7/C2FTrans.

C2FTrans: Coarse-to-Fine Transformers for Medical Image Segmentation

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. In the field of medical image analysis, transformers have also been successfully used in to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. This paper aims to promote awareness of the applications of transformers in medical image analysis. Specifically, we first provide an overview of the core concepts of the attention mechanism built into transformers and other basic components. Second, we review various transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges including the use of transformers in different learning paradigms, improving model efficiency, and coupling with other techniques. We hope this review will provide a comprehensive picture of transformers to readers with an interest in medical image analysis. 

Transformers in Medical Image Analysis: A Review

Cancer-associated fibroblasts: An emerging target against esophageal squamous cell carcinoma.

Aniline is an important biomarker in human exhaled breath for lung cancer, thus aniline sensing is of paramount importance. However, highly sensitive and selective detection of aniline in high moisture environment of exhaled breath is still challenging. In this work, a new fluorescence sensor for aniline with ultrahigh sensitivity is constructed. The o‐carborane modified pyromellitic diimide derivative (CB‐PMI) is designed and prepared as the sensitive layer for the fluorescence sensor. As‐prepared sensing films exhibit a highly improved response toward aniline vapor. The good sensing performance of the fluorescence films in terms of response speed, selectivity, and reusability is also realized, and the detection limit can be as low as 0.1 ppt. The detection linear slope ranges from 1 to 50 ppt. In addition, a homemade portable sensing platform is successfully constructed, which may be a promising prototype for the future detection of modulating breath samples. The reported fluorescence sensor offers a new paradigm for the simple, low cost, and noninvasive diagnosis of lung cancer.

A Portable Fluorescence Sensor with Improved Performance for Aniline Monitoring

Excessive hepatic glucose production (HGP) is a major cause of fasting hyperglycemia in diabetes and antihyperglycemic therapy takes center stage. Nonsteroidal anti-inflammatory drugs (NSAIDs), such as acetylsalicylic acid(Aspirin), reduce hyperglycemia caused by unrestrained gluconeogenesis in diabetes, but its mechanism is incompletely understood. Here, we reported that aspirin lowers fasting blood glucose and hepatic gluconeogenesis, corresponds with lower thromboxane A2 (TXA2) levels, and the hypoglycemic effect of aspirin could be rescued by TP agonist treatment. Upon fasting and diabetes stress, the cyclooxygenase (COX)/TXA2/thromboxane A2 receptor (TP) axis was increased in the livers. TP deficiency suppressed starvation-induced hepatic glucose output, thus inhibiting the progression of diabetes, whereas TP activation promoted gluconeogenesis. Aspirin restrains glucagon signaling and gluconeogenic gene expression (phosphoenolpyruvate carboxykinase (PCK1) and glucose-6-phosphatase (G6Pase)) through TXA2/TP axis. TP mediates hepatic gluconeogenesis by activating PLC/IP3/IP3R signaling, which subsequently enhanced CREB phosphorylation via facilitating CRTC2 nuclear translocation. Thus, our findings demonstrate that TXA2/TP plays a crucial role in aspirin's inhibition of hepatic glucose metabolism, and TP may represent a therapeutic target for diabetes.

Aspirin suppresses hepatic glucagon signaling through decreasing production of thromboxane A2.

The forward kinematics of the Stewart–Gough platform is vital for high-precision positioning and sensing in six degrees of freedom. In this paper, the solving of forward kinematics is converted to searching for the best posture corresponding to the input outrigger length vectors by inverse kinematic calculation. The simulated annealing (SA) process is embedded into particle swarm optimization (PSO) to improve the convergence speed and overcome the local convergence. The proposed SA–PSO algorithm not only accelerates the convergence speed but also improves the reliability and success rate. After the optimization of algorithm parameters, the success rate of the SA–PSO for random outrigger length vector input is steadily above 99.9% while the results of PSO is between 93% and 100%. In the workspace simulation, the average iteration number of the proposed algorithm is reduced from 146.6 to 122.7, meanwhile, the success rate of SA–PSO is 99.992%, much higher than the 93.805% of PSO. Compared with the traditional Newton–Raphson method, the SA–PSO method has a higher success rate and is less time consuming. Finally, a physical experiment is conducted on a six-degree-of-freedom platform, revealing that the given algorithm is reliable and accurate.

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Zihao Yin

Papers

Transformers in Medical Image Analysis: A Review

Cancer-associated fibroblasts: An emerging target against esophageal squamous cell carcinoma.

A Portable Fluorescence Sensor with Improved Performance for Aniline Monitoring

Aspirin suppresses hepatic glucagon signaling through decreasing production of thromboxane A2.

A New Solving Method Based on Simulated Annealing Particle Swarm Optimization for the Forward Kinematic Problem of the Stewart–Gough Platform