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

/pdf/a-survey-on-deep-learning-for-skin-lesion-segmentation-1t57bhxg.pdf

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. 

Chen Gan

Papers

Transformers in Medical Image Analysis: A Review