Reviewerly/exHarmony huggingface dataset:
I4OC open citations implementation in Janeway

One of the strongly recommended criteria under Plan S is that journals provide “Openly accessible data on citations according to the standards by the Initiative for Open Citations (I4OC)”. This means, essentially, depositing citation data with Crossref and then marking it as open. This is a tricky task that will be outside of the ability of many smaller publishers.
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

It is imperative to ensure the robustness of deep learning models in critical applications such as, healthcare. While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks. We present a 3D frequency domain adversarial attack for volumetric medical image segmentation models and demonstrate its advantages over conventional input or voxel domain attacks. Using our proposed attack, we introduce a novel frequency domain adversarial training approach for optimizing a robust model against voxel and frequency domain attacks. Moreover, we propose frequency consistency loss to regulate our frequency domain adversarial training that achieves a better tradeoff between model's performance on clean and adversarial samples. Code is available at https://github.com/asif-hanif/vafa .
A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images

Single particle tracking is an important image analysis technique widely used in biomedical sciences to follow the movement of subcellular structures, which typically appear as individual particles in fluorescence microscopy images. In practice, the low signal-to-noise ratio (SNR) of fluorescence microscopy images as well as the high density and complex movement of subcellular structures pose substantial technical challenges for accurate and robust tracking. In this paper, we propose a novel Transformer-based single particle tracking method called Motion Transformer Tracker (MoTT). By using its attention mechanism to learn complex particle behaviors from past and hypothetical future tracklets (i.e., fragments of trajectories), MoTT estimates the matching probabilities between each live/established tracklet and its multiple hypothesis tracklets simultaneously, as well as the existence probability and position of each live tracklet. Global optimization is then used to find the overall best matching for all live tracklets. For those tracklets with high existence probabilities but missing detections due to e.g., low SNRs, MoTT utilizes its estimated particle positions to substitute for the missed detections, a strategy we refer to as relinking in this study. Experiments have confirmed that this strategy substantially alleviates the impact of missed detections and enhances the robustness of our tracking method. Overall, our method substantially outperforms competing state-of-the-art methods on the ISBI Particle Tracking Challenge datasets. It provides a powerful tool for studying the complex spatiotemporal behavior of subcellular structures. The source code is publicly available at https://github.com/imzhangyd/MoTT.git .
Debiasing Medical Visual Question Answering via Counterfactual Training

Medical Visual Question Answering (Med-VQA) is expected to predict a convincing answer with the given medical image and clinical question, aiming to assist clinical decision-making. While today's works have intention to rely on the superficial linguistic correlations as a shortcut, which may generate emergent dissatisfactory clinic answers. In this paper, we propose a novel DeBiasing Med-VQA model with CounterFactual training (DeBCF) to overcome language priors comprehensively. Specifically, we generate counterfactual samples by masking crucial keywords and assigning irrelevant labels, which implicitly promotes the sensitivity of the model to the semantic words and visual objects for bias-weaken. Furthermore, to explicitly prevent the cheating linguistic correlations, we formulate the language prior into counterfactual causal effects and eliminate it from the total effect on the generated answers. Additionally, we initiatively present a newly splitting bias-sensitive Med-VQA dataset, Semantically-Labeled Knowledge-Enhanced under Changing Priors (SLAKE-CP) dataset through regrouping and re-splitting the train-set and test-set of SLAKE into the different prior distribution of answers, dedicating the model to learn interpretable objects rather than overwhelmingly memorizing biases. Experimental results on two public datasets and SLAKE-CP demonstrate that the proposed DeBCF outperforms existing state-of-the-art Med-VQA models and obtains significant improvement in terms of accuracy and interpretability. To our knowledge, it's the first attempt to overcome language priors in Med-VQA and construct the bias-sensitive dataset for evaluating debiased ability.
Rethinking Semi-Supervised Federated Learning: How to Co-train Fully-Labeled and Fully-Unlabeled Client Imaging Data

The most challenging, yet practical, setting of semi-supervised federated learning (SSFL) is where a few clients have fully labeled data whereas the other clients have fully unlabeled data. This is particularly common in healthcare settings where collaborating partners (typically hospitals) may have images but not annotations. The bottleneck in this setting is the joint training of labeled and unlabeled clients as the objective function for each client varies based on the availability of labels. This paper investigates an alternative way for effective training with labeled and unlabeled clients in a federated setting. We propose a novel learning scheme specifically designed for SSFL which we call Isolated Federated Learning (IsoFed) that circumvents the problem by avoiding simple averaging of supervised and semi-supervised models together. In particular, our training approach consists of two parts - (a) isolated aggregation of labeled and unlabeled client models, and (b) local self-supervised pretraining of isolated global models in all clients. We evaluate our model performance on medical image datasets of four different modalities publicly available within the biomedical image classification benchmark MedMNIST. We further vary the proportion of labeled clients and the degree of heterogeneity to demonstrate the effectiveness of the proposed method under varied experimental settings.
Quarter match non-local means algorithm for noise removal

The notion of improving plays out in many forms in our lives. We look for better quality, faster speed, and leisurelier connections. To achieve our desired goals, we must ask questions. How to make a process stronger? How to make a process more efficient? How to make a process more effective? Image denoising plays a vital role in many professions and understanding how noise can be present in images has led to multiple denoising techniques. These techniques include total variation regularization, non-local regularization, sparse representation, and low-rank minimization just to name a few. Many of these techniques exist because of the concept of improvement. First, we have a change (problem). This change invokes thoughts and questions. How these changes occur and how they are handled play an essential role in the realization or malfunction of that process. With this understanding, first, we look to fully understand the process to achieve success. As it relates to image denoising, the non-local means is incredibly effective in image reconstruction. In particular, the non-local means filter removes noise and sharpens edges without losing too many fine structures and details. Also, the non-local means algorithm is amazingly accurate. Consequently, the disadvantage that plagues the non-local means filtering algorithm is the computational burden and it is due to the non-local averaging. In this paper, we investigate innovative ways to reduce the computational burden and enhance the effectiveness of this filtering process. Research examining image analysis shows there is a battle between noise reduction and the preservation of actual features, which makes the reduction of noise a difficult task. For exploration, we propose a quarter-match non-local means denoising filtering algorithm. The filters help to classify a more concentrated region in the image and thereby enhance the computational efficiency of the existing non-local means denoising methods and produce an enriched comparison for overlying in the restoration process. To survey the constructs of this new algorithm, the authors use the original non-local means filtering algorithm, which is coined, “State of the Art” and other selective processes to test the effectiveness and efficiency of the new model. When comparing the original non-local means with the new quarter match filtering algorithm, on average, we can reduce the computational cost by half, while improving the quality of the image. To further test our new algorithm, medical resonance (MR) and synthetic aperture radar (SAR) images are used as specimens for real-world applications.
Prompt-Based Grouping Transformer for Nucleus Detection and Classification

Automatic nuclei detection and classification can produce effective information for disease diagnosis. Most existing methods classify nuclei independently or do not make full use of the semantic similarity between nuclei and their grouping features. In this paper, we propose a novel end-to-end nuclei detection and classification framework based on a grouping transformer-based classifier. The nuclei classifier learns and updates the representations of nuclei groups and categories via hierarchically grouping the nucleus embeddings. Then the cell types are predicted with the pairwise correlations between categorical embeddings and nucleus features. For the efficiency of the fully transformer-based framework, we take the nucleus group embeddings as the input prompts of backbone, which helps harvest grouping guided features by tuning only the prompts instead of the whole backbone. Experimental results show that the proposed method significantly outperforms the existing models on three datasets.
Diffusion-Based Data Augmentation for Nuclei Image Segmentation

Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of histopathology images. Although fully-supervised deep learning-based methods have made significant progress, a large number of labeled images are required to achieve great segmentation performance. Considering that manually labeling all nuclei instances for a dataset is inefficient, obtaining a large-scale human-annotated dataset is time-consuming and labor-intensive. Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value. In this paper, we introduce the first diffusion-based augmentation method for nuclei segmentation. The idea is to synthesize a large number of labeled images to facilitate training the segmentation model. To achieve this, we propose a two-step strategy. In the first step, we train an unconditional diffusion model to synthesize the Nuclei Structure that is defined as the representation of pixel-level semantic and distance transform. Each synthetic nuclei structure will serve as a constraint on histopathology image synthesis and is further post-processed to be an instance map. In the second step, we train a conditioned diffusion model to synthesize histopathology images based on nuclei structures. The synthetic histopathology images paired with synthetic instance maps will be added to the real dataset for training the segmentation model. The experimental results show that by augmenting 10% labeled real dataset with synthetic samples, one can achieve comparable segmentation results with the fully-supervised baseline.
Towards AI-Driven Radiology Education: A Self-supervised Segmentation-Based Framework for High-Precision Medical Image Editing

Medical education is essential for providing the best patient care in medicine, but creating educational materials using real-world data poses many challenges. For example, the diagnosis and treatment of a disease can be affected by small but significant differences in medical images; however, collecting images to highlight such differences is often costly. Therefore, medical image editing, which allows users to create their intended disease characteristics, can be useful for education. However, existing image-editing methods typically require manually annotated labels, which are labor-intensive and often challenging to represent fine-grained anatomical elements precisely. Herein, we present a novel algorithm for editing anatomical elements using segmentation labels acquired through self-supervised learning. Our self-supervised segmentation achieves pixel-wise clustering under the constraint of invariance to photometric and geometric transformations, which are assumed not to change the clinical interpretation of anatomical elements. The user then edits the segmentation map to produce a medical image with the intended detailed findings. Evaluation by five expert physicians demonstrated that the edited images appeared natural as medical images and that the disease characteristics were accurately reproduced.
Localized Questions in Medical Visual Question Answering

Visual Question Answering (VQA) models aim to answer natural language questions about given images. Due to its ability to ask questions that differ from those used when training the model, medical VQA has received substantial attention in recent years. However, existing medical VQA models typically focus on answering questions that refer to an entire image rather than where the relevant content may be located in the image. Consequently, VQA models are limited in their interpretability power and the possibility to probe the model about specific image regions. This paper proposes a novel approach for medical VQA that addresses this limitation by developing a model that can answer questions about image regions while considering the context necessary to answer the questions. Our experimental results demonstrate the effectiveness of our proposed model, outperforming existing methods on three datasets. Our code and data are available at https://github.com/sergiotasconmorales/locvqa .
Localized Region Contrast for Enhancing Self-supervised Learning in Medical Image Segmentation

Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in applying self-supervised learning to the medical domain, where unlabeled images are abundant and labeled images are difficult to obtain. However, most self-supervised learning approaches are modeled as image level discriminative or generative proxy tasks, which may not capture the finer level representations necessary for dense prediction tasks like multi-organ segmentation. In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation. Our approach involves identifying Super-pixels by Felzenszwalb's algorithm and performing local contrastive learning using a novel contrastive sampling loss. Through extensive experiments on three multi-organ segmentation datasets, we demonstrate that integrating LRC to an existing self-supervised method in a limited annotation setting significantly improves segmentation performance. Moreover, we show that LRC can also be applied to fully-supervised pre-training methods to further boost performance.
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?

While deep neural network models offer unmatched classification performance, they are prone to learning spurious correlations in the data. Such dependencies on confounding information can be difficult to detect using performance metrics if the test data comes from the same distribution as the training data. Interpretable ML methods such as post-hoc explanations or inherently interpretable classifiers promise to identify faulty model reasoning. However, there is mixed evidence whether many of these techniques are actually able to do so. In this paper, we propose a rigorous evaluation strategy to assess an explanation technique's ability to correctly identify spurious correlations. Using this strategy, we evaluate five post-hoc explanation techniques and one inherently interpretable method for their ability to detect three types of artificially added confounders in a chest x-ray diagnosis task. We find that the post-hoc technique SHAP, as well as the inherently interpretable Attri-Net provide the best performance and can be used to reliably identify faulty model behavior.
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps

The success of supervised deep learning models on cell recognition tasks relies on detailed annotations. Many previous works have managed to reduce the dependency on labels. However, considering the large number of cells contained in a patch, costly and inefficient labeling is still inevitable. To this end, we explored label-free methods for cell recognition. Prior self-activation maps (PSM) are proposed to generate pseudo masks as training targets. To be specific, an activation network is trained with self-supervised learning. The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps. Afterward, a semantic clustering module is then introduced as a pipeline to transform PSMs to pixel-level semantic pseudo masks for downstream tasks. We evaluated our method on two histological datasets: MoNuSeg (cell segmentation) and BCData (multi-class cell detection). Compared with other fully-supervised and weakly-supervised methods, our method can achieve competitive performance without any manual annotations. Our simple but effective framework can also achieve multi-class cell detection which can not be done by existing unsupervised methods. The results show the potential of PSMs that might inspire other research to deal with the hunger for labels in medical area.
A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis

Despite the remarkable advances in deep learning for medical image analysis, it has become evident that biases in datasets used for training such models pose considerable challenges for a clinical deployment, including fairness and domain generalization issues. Although the development of bias mitigation techniques has become ubiquitous, the nature of inherent and unknown biases in real-world medical image data prevents a comprehensive understanding of algorithmic bias when developing deep learning models and bias mitigation methods. To address this challenge, we propose a modular and customizable framework for bias simulation in synthetic but realistic medical imaging data. Our framework provides complete control and flexibility for simulating a range of bias scenarios that can lead to undesired model performance and shortcut learning. In this work, we demonstrate how this framework can be used to simulate morphological biases in neuroimaging data for disease classification with a convolutional neural network as a first feasibility analysis. Using this case example, we show how the proportion of bias in the disease class and proximity between disease and bias regions can affect model performance and explainability results. The proposed framework provides the opportunity to objectively and comprehensively study how biases in medical image data affect deep learning pipelines, which will facilitate a better understanding of how to responsibly develop models and bias mitigation methods for clinical use. Code is available at github.com/estanley16/SimBA .
Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis

Fueled by deep learning, computer-aided diagnosis achieves huge advances. However, out of controlled lab environments, algorithms could face multiple challenges. Open set recognition (OSR), as an important one, states that categories unseen in training could appear in testing. In medical fields, it could derive from incompletely collected training datasets and the constantly emerging new or rare diseases. OSR requires an algorithm to not only correctly classify known classes, but also recognize unknown classes and forward them to experts for further diagnosis. To tackle OSR, we assume that known classes could densely occupy small parts of the embedding space and the remaining sparse regions could be recognized as unknowns. Following it, we propose Open Margin Cosine Loss (OMCL) unifying two mechanisms. The former, called Margin Loss with Adaptive Scale (MLAS), introduces angular margin for reinforcing intra-class compactness and inter-class separability, together with an adaptive scaling factor to strengthen the generalization capacity. The latter, called Open-Space Suppression (OSS), opens the classifier by recognizing sparse embedding space as unknowns using proposed feature space descriptors. Besides, since medical OSR is still a nascent field, two publicly available benchmark datasets are proposed for comparison. Extensive ablation studies and feature visualization demonstrate the effectiveness of each design. Compared with state-of-the-art methods, MLAS achieves superior performances, measured by ACC, AUROC, and OSCR.
Subscribe to Open and Plan S

Subscribe to Open is a model pioneered by Annual Reviews that basically says that if libraries continue to subscribe, the title will become OA. If libraries drop out, it goes back to being subscription. A good point that Lisa Janicke Hinchliffe brought to my attention is that this poses problems for the status of the title under Plan S provisions. Is this a type of hybrid publication?
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging

Despite recent progress in enhancing the privacy of federated learning (FL) via differential privacy (DP), the trade-off of DP between privacy protection and performance is still underexplored for real-world medical scenario. In this paper, we propose to optimize the trade-off under the context of client-level DP, which focuses on privacy during communications. However, FL for medical imaging involves typically much fewer participants (hospitals) than other domains (e.g., mobile devices), thus ensuring clients be differentially private is much more challenging. To tackle this problem, we propose an adaptive intermediary strategy to improve performance without harming privacy. Specifically, we theoretically find splitting clients into sub-clients, which serve as intermediaries between hospitals and the server, can mitigate the noises introduced by DP without harming privacy. Our proposed approach is empirically evaluated on both classification and segmentation tasks using two public datasets, and its effectiveness is demonstrated with significant performance improvements and comprehensive analytical studies. Code is available at: https://github.com/med-air/Client-DP-FL .