Efficient convolutional neural network (CNN) architecture design has attracted growing research interests. However, they typically apply single receptive field (RF), small asymmetric RFs, or pyramid RFs to learn different feature representations, still encountering two significant challenges in medical image classification tasks: i) They have limitations in capturing diverse lesion characteristics efficiently, e.g., tiny, coordination, small and salient, which have unique roles on the classification results, especially imbalanced medical image classification. ii) The predictions generated by those CNNs are often unfair/biased, bringing a high risk when employing them to real-world medical diagnosis conditions. To tackle these issues, we develop a new concept, Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields (ERoHPRF), to simultaneously boost medical image classification performance and fairness. This concept aims to mimic the multi-expert consultation mode by applying the well-designed heterogeneous pyramid RF bag to capture lesion characteristics with varying significances effectively via convolution operations with multiple heterogeneous kernel sizes. Additionally, ERoHPRF introduces an expert-like structural reparameterization technique to merge its parameters with the two-stage strategy, ensuring competitive computation cost and inference speed through comparisons to a single RF. To manifest the effectiveness and generalization ability of ERoHPRF, we incorporate it into mainstream efficient CNN architectures. The extensive experiments show that our proposed ERoHPRF maintains a better trade-off than state-of-the-art methods in terms of medical image classification, fairness, and computation overhead. The code of this paper is available at https://github.com/XiaoLing12138/Expert-Like-Reparameterization-of-Heterogeneous-Pyramid-Receptive-Fields.

Salient and small lesions (e.g., microaneurysms on fundus) both play significant roles in real-world disease diagnosis under medical image examinations. Although deep neural networks (DNNs) have achieved promising medical image classification performance, they often have limitations in capturing both salient and small lesion information, restricting performance improvement in imbalanced medical image classification. Recently, with the advent of DNN-based style transfer in medical image generation, the roles of clinical styles have attracted great interest, as they are crucial indicators of lesions. Motivated by this observation, we propose a novel Adaptive Dual-Axis Style-based Recalibration (ADSR) module, leveraging the potential of clinical styles to guide DNNs in effectively learning salient and small lesion information from a dual-axis perspective. ADSR first emphasizes salient lesion information via global style-based adaptation, then captures small lesion information with pixel-wise style-based fusion. We construct an ADSR-Net for imbalanced medical image classification by stacking multiple ADSR modules. Additionally, DNNs typically adopt cross-entropy loss for parameter optimization, which ignores the impacts of class-wise predicted probability distributions. To address this, we introduce a new Class-wise Statistics Loss (CWS) combined with CE to further boost imbalanced medical image classification results. Extensive experiments on five imbalanced medical image datasets demonstrate not only the superiority of ADSR-Net and CWS over state-of-the-art (SOTA) methods but also their improved confidence calibration results. For example, ADSR-Net with the proposed loss significantly outperforms CABNet50 by 21.39% and 27.82% in F1 and B-ACC while reducing 3.31% and 4.57% in ECE and BS on ISIC2018.

Spatial attention (SA) mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, the existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, pyramid pixel context adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling (CCPP) to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization (PN), and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCA network (PPCANet) is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss (CL). The extensive experiments on six medical image datasets show that the PPCANet outperforms state-of-the-art (SOTA) attention-based networks and recent DNNs. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process.

Nuclear cataract (NC) is a prior age-related disease for blindness and vision impairment globally. Anterior segment optical coherence tomography (AS-OCT) image is a new ophthalmology image, which can capture the lens nucleus region clearly compared with other ophthalmic images, e.g., slit lamp images. Clinical research has suggested that features e.g., mean from AS-OCT images have varying correlations with NC severity levels. However, existing convolutional neural network (CNN) based NC classification works have not incorporated the clinical features into the network design to improve the performance. To this end, we propose a novel channel-wise and spatial feature recalibration network (CSFR-Net) to predict NC severity levels automatically, which is built on a stack of channel-wise and spatial feature recalibration (CSFR) modules. In each CSFR module, we construct a channel-wise feature recalibration block and a spatial feature recalibration block to recalibrate intermediate feature maps dynamically. This feature recalibration strategy enables CSFR-Net to highlight feature representations and suppress unnecessary ones in a global-and-local manner. We conduct extensive experiments on a clinical AS-OCT image dataset and CIFAR benchmarks. The results show that our CSFR-Net achieves better performance than state-of-the-art methods with less model complexity.

Nuclear cataract (NC) is a leading eye disease for blindness and vision impairment globally. Accurate and objective NC grading/classification is essential for clinically early intervention and cataract surgery planning. Anterior segment optical coherence tomography (AS-OCT) images are capable of capturing the nucleus region clearly and measuring the opacity of NC quantitatively. Recently, clinical research has suggested that the opacity correlation and repeatability between NC severity levels and the average nucleus density on AS-OCT images is high with the interclass and intraclass analysis. Moreover, clinical research has suggested that opacity distribution is uneven on the nucleus region, indicating that the opacities from different nucleus regions may play different roles in NC diagnosis. Motivated by the clinical priors, this paper proposes a simple yet effective region-based integration-and-recalibration attention (RIR), which integrates multiple feature map region representations and recalibrates the weights of each region via softmax attention adaptively. This region recalibration strategy enables the network to focus on high contribution region representations and suppress less useful ones. We combine the RIR block with the residual block to form a Residual-RIR module, and then a sequence of Residual-RIR modules are stacked to a deep network named region-based integration-and-recalibration network (RIR-Net), to predict NC severity levels automatically. The experiments on a clinical AS-OCT image dataset and two OCT datasets demonstrate that our method outperforms strong baselines and previous state-of-the-art methods. Furthermore, attention weight visualization analysis and ablation studies verify the capability of our RIR-Net for adjusting the relative importance of different regions in feature maps dynamically, agreeing with the clinical research.

Multimedia information processing course includes image processing, text processing, video processing, audio processing, graphics, and animation. Classical multimedia information processing course is to lecture these contents as independent course units, making the course teaching inconsistently and student's learning interest and attention lost easily. Archaeology is a cross-disciplinary field, where an archaeological research project needs to use a variety of multimedia information processing technologies. This paper introduces a novel cross-disciplinary course teaching approach to combine traditional multimedia information processing techniques with archaeological research content in the new engineering era, which increases students' attention and interest in the multimedia information processing course. Teaming up with the archaeology professor, we present two multimedia-archaeology projects: intelligent pottery fragments splicing and intelligent Oracle inscription recognition. We have addressed a few challenges in our courses: 1) how to guide students efficiently to implement different project contents, e.g., using the scanner to acquire three-dimensional porcelain fragment data skillfully. 2) How to inspire students to learn and use various multimedia processing technologies and archaeology knowledge. 3) How the teacher adapts the teaching content according to the dynamic interests of the students. To address these challenges, students are grouped into two project teams based on their strengths and interests. We introduce collaborative learning and active learning strategies to help students learn and use different knowledge and address project problems and learning problems. We also invite the archaeological professor to teach basic archaeology knowledge in the class. Furthermore, to better understand the students' learning situation, we present a weekly project progress report approach, which can also help the teacher adjust the teaching content. This teaching approach can enhance the continuity of multimedia information processing teaching and stimulate students' enthusiasm and creativity in learning. Moreover, it can deepen the cultural atmosphere of the teaching in an engineering course.

Nuclear cataract is one of the most common types of cataract. In the recent, ophthalmologists are increasingly using anterior segment optical coherence tomography (AS-OCT) images to diagnose many ocular diseases including cataract. The relationship between cataract and the lens opacity based on AS-OCT images has been being studied in clinical pioneer research. However, using AS-OCT images to classify cataract automatically based on computer-aided diagnosis (CAD) technique has not been seriously studied. This paper proposes a novel Convolutional Neural Network (CNN) model named GraNet for nuclear cataract classification based on AS-OCT images. In the GraNet, we introduce a grading block to learn high-level feature representations based on the pointwise convolution method. To further improve the classification performance, we propose a simple and efficient cross-training method is comprised of focal loss and cross-entropy loss. Extensive experiments are conducted on the AS-OCT image dataset, the results demonstrate that the proposed methods achieve better nuclear cataract classification results than baselines.