The main advantage of this approach is its efficiency at inference time with comparable performance of B-spline based approach where an optimization is needed per registration. And it has, according to the authors, much less parameters to optimize. Please confirm if this understanding is correct? 2. What is the reason of making the choice of using multiple steps to gradually transform the moving image to the fixed one? Could the local transformation done in one step instead? For instance, the position network could directly predict K locations to transform in one step instead of prediction one location for K steps.

The paper seems to contribute in a significant way in proposing an alternative RNN-based approach for deformable image registration. Although the experimental setting is not extremely strong, the proposed approach seems to give significant computational advantages. Rebuttal clarified most of the reviewers concerns.

The prototypical parts network presented in this work is original and potentially very useful learning framework for domains where process-based interpretability is critical. The method is thoroughly evaluated against alternative approaches and performs comparable to other state-of-the-art interpretable learning algorithms. The paper is well written, well motivated, and is accompanied by empirical results to validate the algorithmic contributions. Overall, I would recommend this paper for acceptance. One place for improvement is the discussion of this work in the context of alternative interpretable approaches, specifically the methods that show comparable accuracy.

The reviewers agreed that this paper includes interesting ideas and is well written. Please read the remaining reviewers' comments carefully for your final version.

The paper presents a neural network model for image registration, which generates an arbitrary displacement field to transform the input image in a way that matches the target. This neural network has several components, including a common feature extraction model that results in a 4D tensor with the correlations of local features from both images. The tensor is then transformed into a vector representation of the transformation, and later used to reconstruct a displacement field. COMMENTS Overall, the work is relatively well presented and provides details to understand most of the formulation and solution. However, there are some confusing aspects that could be clarified or stated more prominently.

This submission received mixed ratings. The most positive reviewers has a non confident rating. R1 and R2 appreciate that the paper is well written and presents an interesting approach to image registration. R1 and R3 point out that the central contribution is not clearly stated in the text. Also overlap of text in sections 3.1-3.3

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.

Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for their ability to capture long-range dependency via self-attention (SA). However, the high computation and memory loads of SA (growing quadratically with the spatial resolution) hinder transformers from processing subtle textural information in high-resolution image features, e.g., at the full and half image resolutions. This limits deformable registration as the high-resolution textural information is crucial for finding precise pixel-wise correspondence between subtle anatomical structures. Cross-covariance Attention (XCA), as a "transposed" version of SA that operates across feature channels, has complexity growing linearly with the spatial resolution, providing the feasibility of capturing long-range dependency among high-resolution image features. However, existing XCA-based transformers merely capture coarse global long-range dependency, which are unsuitable for deformable image registration relying primarily on fine-grained local correspondence. In this study, we propose to improve existing deep learning-based registration methods by embedding a new XCA mechanism. To this end, we design an XCA-based transformer block optimized for deformable medical image registration, named Multi-Axis XCA (MAXCA). Our MAXCA serves as a general network block that can be embedded into various registration network architectures. It can capture both global and local long-range dependency among high-resolution image features by applying regional and dilated XCA in parallel via a multi-axis design. Extensive experiments on two well-benchmarked inter-/intra-patient registration tasks with seven public medical datasets demonstrate that our MAXCA block enables state-of-the-art registration performance.

Superresolution theory and techniques seek to recover signals from samples in the presence of blur and noise. Discrete image registration can be an approach to fuse information from different sets of samples of the same signal. Quantization errors in the spatial domain are inherent to digital images. We consider superresolution and discrete image registration for one-dimensional spatially-limited piecewise constant functions which are subject to blur which is Gaussian or a mixture of Gaussians as well as to round-off errors. We describe a signal-dependent measurement matrix which captures both types of effects. For this setting we show that the difficulties in determining the discontinuity points from two sets of samples even in the absence of other types of noise. If the samples are also subject to statistical noise, then it is necessary to align and segment the data sequences to make the most effective inferences about the amplitudes and discontinuity points. Under some conditions on the blur, the noise, and the distance between discontinuity points, we prove that we can correctly align and determine the first samples following each discontinuity point in two data sequences with an approach based on dynamic programming.

The p16/Ki-67 dual staining method is a new approach for cervical cancer screening with high sensitivity and specificity. However, there are issues of mis-detection and inaccurate recognition when the YOLOv5s algorithm is directly applied to dual-stained cell images. This paper Proposes a novel cervical cancer dual-stained image recognition (DSIR-YOLO) model based on an YOLOv5. By fusing the Swin-Transformer module, GAM attention mechanism, multi-scale feature fusion, and EIoU loss function, the detection performance is significantly improved, with mAP@0.5 and mAP@0.5:0.95 reaching 92.6% and 70.5%, respectively. Compared with YOLOv5s in five-fold cross-validation, the accuracy, recall, mAP@0.5, and mAP@0.5:0.95 of the improved algorithm are increased by 2.3%, 4.1%, 4.3%, and 8.0%, respectively, with smaller variances and higher stability. Compared with other detection algorithms, DSIR-YOLO in this paper sacrifices some performance requirements to improve the network recognition effect. In addition, the influence of dataset quality on the detection results is studied. By controlling the sealing property of pixels, scale difference, unlabelled cells, and diagonal annotation, the model detection accuracy, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 13.3%, 15.3%, 18.3%, and 30.5%, respectively.