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Generative self-supervised learning demonstrates outstanding representation learning capabilities in both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, there are currently no generative pre-training methods related to selective state space models (Mamba) that can handle long-range dependencies effectively. To address this challenge, we introduce a generative self-supervised learning method for Mamba (MambaMIM) based on Selective Structure State Space Sequence Token-interpolation (S6T), a general-purpose pre-training method for arbitrary Mamba architectures. Our method, MambaMIM, incorporates a bottom-up 3D hybrid masking strategy in the encoder to maintain masking consistency across different architectures. Additionally, S6T is employed to learn causal relationships between the masked sequence in the state space. MambaMIM can be used on any single or hybrid Mamba architectures to enhance the Mamba long-range representation capability. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for pre-training medical image tasks. The code is available at: https://github.com/FengheTan9/MambaMIM, Comment: 10 pages, 7 figures

The generative self-supervised learning strategy exhibits remarkable learning representational capabilities. However, there is limited attention to end-to-end pre-training methods based on a hybrid architecture of CNN and Transformer, which can learn strong local and global representations simultaneously. To address this issue, we propose a generative pre-training strategy called Hybrid Sparse masKing (HySparK) based on masked image modeling and apply it to large-scale pre-training on medical images. First, we perform a bottom-up 3D hybrid masking strategy on the encoder to keep consistency masking. Then we utilize sparse convolution for the top CNNs and encode unmasked patches for the bottom vision Transformers. Second, we employ a simple hierarchical decoder with skip-connections to achieve dense multi-scale feature reconstruction. Third, we implement our pre-training method on a collection of multiple large-scale 3D medical imaging datasets. Extensive experiments indicate that our proposed pre-training strategy demonstrates robust transfer-ability in supervised downstream tasks and sheds light on HySparK's promising prospects. The code is available at https://github.com/FengheTan9/HySparK, Comment: Early accept at MICCAI 2024

Large Language Models have excelled in various fields but encounter challenges in memory and time efficiency due to the expanding Key-Value (KV) cache required for long-sequence inference. Recent efforts try to reduce KV cache size to a given memory budget by evicting vast non-critical cache elements during runtime, while preserving generation quality. Our revisiting of current eviction methods reveals that they fundamentally minimize an upper bound of the $L_1$ eviction loss between the pre- and post-eviction outputs of multi-head self-attention mechanisms. Moreover, our analysis indicates that the common practices of uniformly assigning budgets across attention heads harm their post-eviction generation quality. In light of these findings, we propose a simple yet effective adaptive budget allocation algorithm. This algorithm not only optimizes the theoretical loss upper bound but also reduces the $L_1$ eviction loss in practice by aligning with the varied characteristics across different heads. By integrating this algorithm into two state-of-the-art methods, we demonstrate the effectiveness of using adaptive budget allocation to optimize KV cache eviction. Extensive evaluations on 16 datasets and the Needle-in-a-Haystack test confirm significant performance improvements across various tasks.

The advent of foundation models (FMs) in healthcare offers unprecedented opportunities to enhance medical diagnostics through automated classification and segmentation tasks. However, these models also raise significant concerns about their fairness, especially when applied to diverse and underrepresented populations in healthcare applications. Currently, there is a lack of comprehensive benchmarks, standardized pipelines, and easily adaptable libraries to evaluate and understand the fairness performance of FMs in medical imaging, leading to considerable challenges in formulating and implementing solutions that ensure equitable outcomes across diverse patient populations. To fill this gap, we introduce FairMedFM, a fairness benchmark for FM research in medical imaging.FairMedFM integrates with 17 popular medical imaging datasets, encompassing different modalities, dimensionalities, and sensitive attributes. It explores 20 widely used FMs, with various usages such as zero-shot learning, linear probing, parameter-efficient fine-tuning, and prompting in various downstream tasks -- classification and segmentation. Our exhaustive analysis evaluates the fairness performance over different evaluation metrics from multiple perspectives, revealing the existence of bias, varied utility-fairness trade-offs on different FMs, consistent disparities on the same datasets regardless FMs, and limited effectiveness of existing unfairness mitigation methods. Checkout FairMedFM's project page and open-sourced codebase, which supports extendible functionalities and applications as well as inclusive for studies on FMs in medical imaging over the long term., Comment: 29 pages, 17 figures

Invariant-based Contrastive Learning (ICL) methods have achieved impressive performance across various domains. However, the absence of latent space representation for distortion (augmentation)-related information in the latent space makes ICL sub-optimal regarding training efficiency and robustness in downstream tasks. Recent studies suggest that introducing equivariance into Contrastive Learning (CL) can improve overall performance. In this paper, we rethink the roles of augmentation strategies and equivariance in improving CL efficacy. We propose a novel Equivariant-based Contrastive Learning (ECL) framework, CLeVER (Contrastive Learning Via Equivariant Representation), compatible with augmentation strategies of arbitrary complexity for various mainstream CL methods and model frameworks. Experimental results demonstrate that CLeVER effectively extracts and incorporates equivariant information from data, thereby improving the training efficiency and robustness of baseline models in downstream tasks., Comment: Preprint. Under review

Emerging unsupervised reconstruction techniques based on implicit neural representation (INR), such as NeRP, CoIL, and SCOPE, have shown unique capabilities in CT linear inverse imaging. In this work, we propose a novel unsupervised density neural representation (Diner) to tackle the challenging problem of CT metal artifacts when scanned objects contain metals. The drastic variation of linear attenuation coefficients (LACs) of metals over X-ray spectra leads to a nonlinear beam hardening effect (BHE) in CT measurements. Recovering CT images from metal-affected measurements therefore poses a complicated nonlinear inverse problem. Existing metal artifact reduction (MAR) techniques mostly formulate the MAR as an image inpainting task, which ignores the energy-induced BHE and produces suboptimal performance. Instead, our Diner introduces an energy-dependent polychromatic CT forward model to the INR framework, addressing the nonlinear nature of the MAR problem. Specifically, we decompose the energy-dependent LACs into energy-independent densities and energy-dependent mass attenuation coefficients (MACs) by fully considering the physical model of X-ray absorption. Using the densities as pivot variables and the MACs as known prior knowledge, the LACs can be accurately reconstructed from the raw measurements. Technically, we represent the unknown density map as an implicit function of coordinates. Combined with a novel differentiable forward model simulating the physical acquisition from the densities to the measurements, our Diner optimizes a multi-layer perception network to approximate the implicit function by minimizing predicted errors between the estimated and real measurements. Experimental results on simulated and real datasets confirm the superiority of our unsupervised Diner against popular supervised techniques in MAR performance and robustness., Comment: 13 pages

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets., Comment: 16 pages, 11 figures

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error., Comment: Accepted at Medical Image Analysis Journal

With the recent advancements in single-image-based human mesh recovery, there is a growing interest in enhancing its performance in certain extreme scenarios, such as occlusion, while maintaining overall model accuracy. Although obtaining accurately annotated 3D human poses under occlusion is challenging, there is still a wealth of rich and precise 2D pose annotations that can be leveraged. However, existing works mostly focus on directly leveraging 2D pose coordinates to estimate 3D pose and mesh. In this paper, we present PostoMETRO($\textbf{Pos}$e $\textbf{to}$ken enhanced $\textbf{ME}$sh $\textbf{TR}$ansf$\textbf{O}$rmer), which integrates occlusion-resilient 2D pose representation into transformers in a token-wise manner. Utilizing a specialized pose tokenizer, we efficiently condense 2D pose data to a compact sequence of pose tokens and feed them to the transformer together with the image tokens. This process not only ensures a rich depiction of texture from the image but also fosters a robust integration of pose and image information. Subsequently, these combined tokens are queried by vertex and joint tokens to decode 3D coordinates of mesh vertices and human joints. Facilitated by the robust pose token representation and the effective combination, we are able to produce more precise 3D coordinates, even under extreme scenarios like occlusion. Experiments on both standard and occlusion-specific benchmarks demonstrate the effectiveness of PostoMETRO. Qualitative results further illustrate the clarity of how 2D pose can help 3D reconstruction. Code will be made available.

Weakly supervised surgical instrument segmentation with only instrument presence labels has been rarely explored in surgical domain. To mitigate the highly under-constrained challenges, we extend a two-stage weakly supervised segmentation paradigm with temporal attributes from two perspectives. From a temporal equivariance perspective, we propose a prototype-based temporal equivariance regulation loss to enhance pixel-wise consistency between adjacent features. From a semantic continuity perspective, we propose a class-aware temporal semantic continuity loss to constrain the semantic consistency between a global view of target frame and local non-discriminative regions of adjacent reference frame. To the best of our knowledge, WeakSurg is the first instrument-presence-only weakly supervised segmentation architecture to take temporal information into account for surgical scenarios. Extensive experiments are validated on Cholec80, an open benchmark for phase and instrument recognition. We annotate instance-wise instrument labels with fixed time-steps which are double checked by a clinician with 3-years experience. Our results show that WeakSurg compares favorably with state-of-the-art methods not only on semantic segmentation metrics but also on instance segmentation metrics.

Fully-supervised airway segmentation has accomplished significant triumphs over the years in aiding pre-operative diagnosis and intra-operative navigation. However, full voxel-level annotation constitutes a labor-intensive and time-consuming task, often plagued by issues such as missing branches, branch annotation discontinuity, or erroneous edge delineation. label-efficient solutions for airway extraction are rarely explored yet primarily demanding in medical practice. To this end, we introduce a novel skeleton-level annotation (SkA) tailored to the airway, which simplifies the annotation workflow while enhancing annotation consistency and accuracy, preserving the complete topology. Furthermore, we propose a skeleton-supervised learning framework to achieve accurate airway segmentation. Firstly, a dual-stream buffer inference is introduced to realize initial label propagation from SkA, avoiding the collapse of direct learning from SkA. Then, we construct a geometry-aware dual-path propagation framework (GDP) to further promote complementary propagation learning, composed of hard geometry-aware propagation learning and soft geometry-aware propagation guidance. Experiments reveal that our proposed framework outperforms the competing methods with SKA, which amounts to only 1.96% airways, and achieves comparable performance with the baseline model that is fully supervised with 100% airways, demonstrating its significant potential in achieving label-efficient segmentation for other tubular structures, such as vessels.

Multi-contrast (MC) Magnetic Resonance Imaging (MRI) reconstruction aims to incorporate a reference image of auxiliary modality to guide the reconstruction process of the target modality. Known MC reconstruction methods perform well with a fully sampled reference image, but usually exhibit inferior performance, compared to single-contrast (SC) methods, when the reference image is missing or of low quality. To address this issue, we propose DuDoUniNeXt, a unified dual-domain MRI reconstruction network that can accommodate to scenarios involving absent, low-quality, and high-quality reference images. DuDoUniNeXt adopts a hybrid backbone that combines CNN and ViT, enabling specific adjustment of image domain and k-space reconstruction. Specifically, an adaptive coarse-to-fine feature fusion module (AdaC2F) is devised to dynamically process the information from reference images of varying qualities. Besides, a partially shared shallow feature extractor (PaSS) is proposed, which uses shared and distinct parameters to handle consistent and discrepancy information among contrasts. Experimental results demonstrate that the proposed model surpasses state-of-the-art SC and MC models significantly. Ablation studies show the effectiveness of the proposed hybrid backbone, AdaC2F, PaSS, and the dual-domain unified learning scheme., Comment: 11 pages, 4 figures, 2 tables

Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort has been dedicated to mitigating unfairness in the training datasets or procedures. However, with the increasing prevalence of foundation models in medical image analysis, it is hard to train fair models from scratch while preserving utility. In this paper, we propose a novel method, Adversarial Privacy-aware Perturbations on Latent Embedding (APPLE), that can improve the fairness of deployed segmentors by introducing a small latent feature perturber without updating the weights of the original model. By adding perturbation to the latent vector, APPLE decorates the latent vector of segmentors such that no fairness-related features can be passed to the decoder of the segmentors while preserving the architecture and parameters of the segmentor. Experiments on two segmentation datasets and five segmentors (three U-Net-like and two SAM-like) illustrate the effectiveness of our proposed method compared to several unfairness mitigation methods.

With the proposal of the Segment Anything Model (SAM), fine-tuning SAM for medical image segmentation (MIS) has become popular. However, due to the large size of the SAM model and the significant domain gap between natural and medical images, fine-tuning-based strategies are costly with potential risk of instability, feature damage and catastrophic forgetting. Furthermore, some methods of transferring SAM to a domain-specific MIS through fine-tuning strategies disable the model's prompting capability, severely limiting its utilization scenarios. In this paper, we propose an Auto-Prompting Module (APM), which provides SAM-based foundation model with Euclidean adaptive prompts in the target domain. Our experiments demonstrate that such adaptive prompts significantly improve SAM's non-fine-tuned performance in MIS. In addition, we propose a novel non-invasive method called Incremental Pattern Shifting (IPS) to adapt SAM to specific medical domains. Experimental results show that the IPS enables SAM to achieve state-of-the-art or competitive performance in MIS without the need for fine-tuning. By coupling these two methods, we propose ProMISe, an end-to-end non-fine-tuned framework for Promptable Medical Image Segmentation. Our experiments demonstrate that both using our methods individually or in combination achieves satisfactory performance in low-cost pattern shifting, with all of SAM's parameters frozen.

In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner S5P, designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. S5P offers high partitioning quality by segregating the graph's edge set into two subsets, head and tail sets. Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on PowerGraph., Comment: This paper has been accepted by SIGMOD 2024

The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs, focusing on image-text alignment. However, existing methods primarily rely on cosine similarity for alignment, which may not fully capture the complex relationship between medical images and reports. To address this gap, we introduce a novel approach called Cross-Attention Alignment for Radiology Zero-Shot Classification (CARZero). Our approach innovatively leverages cross-attention mechanisms to process image and report features, creating a Similarity Representation that more accurately reflects the intricate relationships in medical semantics. This representation is then linearly projected to form an image-text similarity matrix for cross-modality alignment. Additionally, recognizing the pivotal role of prompt selection in zero-shot learning, CARZero incorporates a Large Language Model-based prompt alignment strategy. This strategy standardizes diverse diagnostic expressions into a unified format for both training and inference phases, overcoming the challenges of manual prompt design. Our approach is simple yet effective, demonstrating state-of-the-art performance in zero-shot classification on five official chest radiograph diagnostic test sets, including remarkable results on datasets with long-tail distributions of rare diseases. This achievement is attributed to our new image-text alignment strategy, which effectively addresses the complex relationship between medical images and reports. Code and models are available at https://github.com/laihaoran/CARZero.

Implicit visual knowledge in a large latent diffusion model (LLDM) pre-trained on natural images is rich and hypothetically universal to natural and medical images. To test this hypothesis from a practical perspective, we propose a novel framework for undersampled MRI Reconstruction by Prompting a large latent Diffusion model (MRPD). While the existing methods trained on MRI datasets are typically of limited generalizability toward diverse data acquisition scenarios, MRPD supports unsupervised and universally adaptive MRI reconstruction. For unsupervised reconstruction, MRSampler guides LLDM with a random-phase-modulated hard-to-soft control. With any single- or multiple-source MRI dataset, MRPD's performance is boosted universally by a lightweight MRAdapter that only finetunes the LLDM's autoencoder. Experiments on FastMRI and IXI show that MRPD is the only model that supports both MRI database-free and database-available scenarios and attains the best generalizability towards out-of-domain (OOD) samplings, contrasts, and organs among compared unsupervised, supervised, and MRI diffusion methods. To our knowledge, MRPD is the first method that empirically shows the universal prowess of an LLDM pre-trained on vast natural images for MRI. Our official implementation is at https://github.com/Z7Gao/MRPD., Comment: 10 pages, 5 figures, 7 tables, 1 pseudocode

When deploying a trained machine learning model in the real world, it is inevitable to receive inputs from out-of-distribution (OOD) sources. For instance, in continual learning settings, it is common to encounter OOD samples due to the non-stationarity of a domain. More generally, when we have access to a set of test inputs, the existing rich line of OOD detection solutions, especially the recent promise of distance-based methods, falls short in effectively utilizing the distribution information from training samples and test inputs. In this paper, we argue that empirical probability distributions that incorporate geometric information from both training samples and test inputs can be highly beneficial for OOD detection in the presence of test inputs available. To address this, we propose to model OOD detection as a discrete optimal transport problem. Within the framework of optimal transport, we propose a novel score function known as the \emph{conditional distribution entropy} to quantify the uncertainty of a test input being an OOD sample. Our proposal inherits the merits of certain distance-based methods while eliminating the reliance on distribution assumptions, a-prior knowledge, and specific training mechanisms. Extensive experiments conducted on benchmark datasets demonstrate that our method outperforms its competitors in OOD detection.

Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We present a novel computational approach, called as O-PRESS, for boosting the axial resolution of OCT with Prior Guidance, a Recurrent mechanism, and Equivariant Self-Supervision. Diverging from conventional superresolution methods that rely on physical models or data-driven techniques, our method seamlessly integrates OCT modeling and deep learning, enabling us to achieve real-time axial-resolution enhancement exclusively from measurements without a need for paired images. Our approach solves two primary tasks of resolution enhancement and noise reduction with one treatment. Both tasks are executed in a self-supervised manner, with equivariance imaging and free space priors guiding their respective processes. Experimental evaluations, encompassing both quantitative metrics and visual assessments, consistently verify the efficacy and superiority of our approach, which exhibits performance on par with fully supervised methods. Importantly, the robustness of our model is affirmed, showcasing its dual capability to enhance axial resolution while concurrently improving the signal-to-noise ratio.