Tong Xin

Few-shot medical image segmentation (FSMIS) has become one of the potential solutions for limited annotated medical image analysis. However, the realistic multiple domains of medical data demands the FSMIS models generalizing across domains. Thus, the cross-domain few-shot medical image segmentation (CD-FSMIS) is introduced, and we propose the Adversarial Prototypical Perturbation (APP) model which employs the adversarial learning strategy in the prototype learning process for gaining the domain robust prototypes. Specifically, the method consists of two components: adversarial signal formulation (ASF) and interactive prototypical attack (IPA). The ASF module collects perturbations from the gained gradients from both of the intra-class variation measurement loss and the inter-class variation measurement loss, and the IPA module aims to impose gained perturbations on the prototypical representation construction process with the two stages of interactive attacking manner. Additionally, a local-imbalance aware whitening loss is designed to resist the shift-sensitive local components for further enforcing to learn the domain robust prototypical representation in the IPA module. Extensive experiments are conducted on three cross-domain medical imaging datasets, and the results demonstrate that our model outperforms the state-of-the-art few-shot medical image segmentation methods. The code is available at https://github.com/YazhouZhu19/APP .

This paper presents a novel approach, Spectral-Interpretable and -Enhanced Transformer (SIEFormer), which leverages spectral analysis to reinterpret the attention mechanism within Vision Transformer (ViT) and enhance feature adaptability, with particular emphasis on challenging Generalized Category Discovery (GCD) tasks. The proposed SIEFormer is composed of two main branches, each corresponding to an implicit and explicit spectral perspective of the ViT, enabling joint optimization. The implicit branch realizes the use of different types of graph Laplacians to model the local structure correlations of tokens, along with a novel Band-adaptive Filter (BaF) layer that can flexibly perform both band-pass and band-reject filtering. The explicit branch, on the other hand, introduces a Maneuverable Filtering Layer (MFL) that learns global dependencies among tokens by applying the Fourier transform to the input ``value" features, modulating the transformed signal with a set of learnable parameters in the frequency domain, and then performing an inverse Fourier transform to obtain the enhanced features. Extensive experiments reveal state-of-the-art performance on multiple image recognition datasets, reaffirming the superiority of our approach through ablation studies and visualizations.

Conventional biomedical image segmentation heavily relies on substantial annotations, which demand significant human and financial resources for collection. Consequently, learning a model with excellent performance using limited medical image data becomes a challenging problem. Upliftingly, the advent of few-shot medical image segmentation (FSMIS) offers a potential solution. Although prototypical networks are commonly employed in existing FSMIS tasks, the prototypes derived from support features often induce significant bias issues caused by intra-class variations. To this end, we propose a method called Cascaded Altering Refinement Transformer (CART) to iteratively calibrate the prototypes with both support and query features. This method focuses on capturing the commonality between foreground information of the support and query features using the Alterating Refinement Transformer (ART) module, which includes two Multi-head Cross Attention (MCA) modules. Furthermore, we cascade ART modules to refine the class prototypes, resulting in representative prototypes. This process ultimately contributes to a more accurate predicted mask. Besides, to preserve more valid information in each cascaded ART module and achieve better performance, we propose a novel inference method that accumulates the predicted segmentation masks in all ART modules by applying the Rounding-Up strategy. Extensive experiments on three public medical image datasets demonstrate that our model outperforms the state-of-the-art methods, and detailed analysis also validates the reasonableness of this design. Code is available at: https://github.com/zmcheng9/CART .

Insects play a vital role in ecosystem functioning, but in some parts of the world, their populations have declined significantly in recent decades due to environmental change, agricultural intensification and other anthropogenic drivers. Monitoring insect populations is crucial for understanding and mitigating biodiversity loss, especially in agro‐ecosystems where a focus on pests and beneficial insects is gaining momentum in the context of sustainable food systems. Biomonitoring has long played an important role in providing early warnings of insect pests and their vectored pathogens and for assessing agro‐ecosystem management. However, identification of invertebrates by taxonomists is time‐consuming and fraught with numerous other challenges, particularly when it comes to real‐time monitoring. Recent technological advances in both computational image recognition and molecular ecology have enhanced biomonitoring efficiency and accuracy, reducing labour efforts, but leading to unprecedented volumes of data generated. This perspective article examines the significance and further potential of deep learning with image‐based recognition, aided by complementary technologies, in advancing entomological biomonitoring. Using entomological sticky traps as an example, we discuss each step of the workflow required to create ecological networks using image‐based recognition, multimodal data and deep learning, and we identify the challenges inherent to this method and other insect survey techniques. In order to become mainstream for global biomonitoring, access to long‐term, standardised multimodal data is required for comprehending ecosystem dynamics, structure and function and for mitigating insect population declines.

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI.

Automatic medical image segmentation has witnessed significant development with the success of large models on massive datasets. However, acquiring and annotating vast medical image datasets often proves to be impractical due to the time consumption, specialized expertise requirements, and compliance with patient privacy standards, etc. As a result, Few-shot Medical Image Segmentation (FSMIS) has become an increasingly compelling research direction. Conventional FSMIS methods usually learn prototypes from support images and apply nearest-neighbor searching to segment the query images. However, only a single prototype cannot well represent the distribution of each class, thus leading to restricted performance. To address this problem, we propose to Generate Multiple Representative Descriptors (GMRD), which can comprehensively represent the commonality within the corresponding class distribution. In addition, we design a Multiple Affinity Maps based Prediction (MAMP) module to fuse the multiple affinity maps generated by the aforementioned descriptors. Furthermore, to address intra-class variation and enhance the representativeness of descriptors, we introduce two novel losses. Notably, our model is structured as a dual-path design to achieve a balance between foreground and background differences in medical images. Extensive experiments on four publicly available medical image datasets demonstrate that our method outperforms the state-of-the-art methods, and the detailed analysis also verifies the effectiveness of our designed module.

Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulting from the use of different acquisition protocols for different patients. In this paper, we introduce a more promising few-shot learning-based method named Region-enhanced Prototypical Transformer (RPT) to mitigate the effects of large intra-class diversity/bias. First, a subdivision strategy is introduced to produce a collection of regional prototypes from the foreground of the support prototype. Second, a self-selection mechanism is proposed to incorporate into the Bias-alleviated Transformer (BaT) block to suppress or remove interferences present in the query prototype and regional support prototypes. By stacking BaT blocks, the proposed RPT can iteratively optimize the generated regional prototypes and finally produce rectified and more accurate global prototypes for Few-Shot Medical Image Segmentation (FSMS). Extensive experiments are conducted on three publicly available medical image datasets, and the obtained results show consistent improvements compared to state-of-the-art FSMS methods. The source code is available at: https://github.com/YazhouZhu19/RPT.