医学影像

iToF is a prevalent cost-effective technology for 3D perception. While its reliance on multi-measurement commonly leads to reduced performance in dynamic environments. Based on the analysis of the physical iToF imaging process we propose the iToF flow composed of crossmode transformation and uni-mode photometric correction to model the variation of measurements caused by different measurement modes and 3D motion respectively. We propose a local linear transform (LLT) based cross-mode transfer module (LCTM) for mode-varying and pixel shift compensation of cross-mode flow and uni-mode photometric correct module (UPCM) for estimating the depth-wise motion caused photometric residual of uni-mode flow. The iToF flow-based depth extraction network is proposed which could facilitate the estimation of the 4-phase measurements at each individual time for high framerate and accurate depth estimation. Extensive experiments including both simulation and real-world experiments are conducted to demonstrate the effectiveness of the proposed methods. Compared with the SOTA method our approach reduces the computation time by 75% while improving the performance by 38%. The code and database are available at https://github.com/ComputationalPerceptionLab/iToF_flow.

Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy. A promising strategy to overcome this challenge is wavefront modulation which induces measurement diversity during image acquisition. Despite its importance designing optimal wavefront modulations to image through scattering remains under-explored. This paper introduces a novel learning-based framework to address the gap. Our approach jointly optimizes wavefront modulations and a computationally lightweight feedforward "proxy" reconstruction network. This network is trained to recover scenes obscured by scattering using measurements that are modified by these modulations. The learned modulations produced by our framework generalize effectively to unseen scattering scenarios and exhibit remarkable versatility. During deployment the learned modulations can be decoupled from the proxy network to augment other more computationally expensive restoration algorithms. Through extensive experiments we demonstrate our approach significantly advances the state of the art in imaging through scattering media. Our project webpage is at https://wavemo-2024.github.io/.

Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However the lack of high-level semantics in pre-training still heavily hinders the performance of downstream tasks. We observe that 3D medical images contain relatively consistent contextual position information i.e. consistent geometric relations between different organs which leads to a potential way for us to learn consistent semantic representations in pre-training. In this paper we propose a simple-yet-effective Volume Contrast (VoCo) framework to leverage the contextual position priors for pre-training. Specifically we first generate a group of base crops from different regions while enforcing feature discrepancy among them where we employ them as class assignments of different regions. Then we randomly crop sub-volumes and predict them belonging to which class (located at which region) by contrasting their similarity to different base crops which can be seen as predicting contextual positions of different sub-volumes. Through this pretext task VoCo implicitly encodes the contextual position priors into model representations without the guidance of annotations enabling us to effectively improve the performance of downstream tasks that require high-level semantics. Extensive experimental results on six downstream tasks demonstrate the superior effectiveness of VoCo. Code will be available at https://github.com/Luffy03/VoCo.

The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However its application in medical imaging presents challenges requiring either substantial training costs and extensive medical datasets for full model fine-tuning or high-quality prompts for optimal performance. This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure. In the initial stage H-SAM employs SAM's original decoder to generate a prior probabilistic mask guiding a more intricate decoding process in the second stage. Specifically we propose two key designs: 1) A class-balanced mask-guided self-attention mechanism addressing the unbalanced label distribution enhancing image embedding; 2) A learnable mask cross-attention mechanism spatially modulating the interplay among different image regions based on the prior mask. Moreover the inclusion of a hierarchical pixel decoder in H-SAM enhances its proficiency in capturing fine-grained and localized details. This approach enables SAM to effectively integrate learned medical priors facilitating enhanced adaptation for medical image segmentation with limited samples. Our H-SAM demonstrates a 4.78% improvement in average Dice compared to existing prompt-free SAM variants for multi-organ segmentation using only 10% of 2D slices. Notably without using any unlabeled data H-SAM even outperforms state-of-the-art semi-supervised models relying on extensive unlabeled training data across various medical datasets. Our code is available at https://github.com/Cccccczh404/H-SAM.

Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g. 224 x 224 pixels) but scaling these models to learn slide embeddings from the entirety of giga-pixel whole-slide images (WSIs) remains challenging. Here we leverage complementary information from gene expression profiles to guide slide representation learning using multi-modal pre-training. Expression profiles constitute highly detailed molecular descriptions of a tissue that we hypothesize offer a strong task-agnostic training signal for learning slide embeddings. Our slide and expression (S+E) pretraining strategy called TANGLE employs modality-specific encoders the outputs of which are aligned via contrastive learning. TANGLE was pre-trained on samples from three different organs: liver (n=6597 S+E pairs) breast (n=1020) and lung (n=1012) from two different species (Homo sapiens and Rattus norvegicus). Across three independent test datasets consisting of 1265 breast WSIs 1946 lung WSIs and 4584 liver WSIs TANGLE shows significantly better few-shot performance compared to supervised and SSL baselines. When assessed using prototype-based classification and slide retrieval TANGLE also shows a substantial performance improvement over all baselines. Code available at https://github.com/mahmoodlab/TANGLE.

A major focus of clinical imaging workflow is disease diagnosis and management leading to medical imaging datasets strongly tied to specific clinical objectives. This scenario has led to the prevailing practice of developing task-specific segmentation models without gaining insights from widespread imaging cohorts. Inspired by the training program of medical radiology residents we propose a shift towards universal medical image segmentation a paradigm aiming to build medical image understanding foundation models by leveraging the diversity and commonality across clinical targets body regions and imaging modalities. Towards this goal we develop Hermes a novel context-prior learning approach to address the challenges of data heterogeneity and annotation differences in medical image segmentation. In a large collection of eleven diverse datasets (2438 3D images) across five modalities (CT PET T1 T2 and cine MRI) and multiple body regions we demonstrate the merit of the universal paradigm over the traditional paradigm on addressing multiple tasks within a single model. By exploiting the synergy across tasks Hermes achieves state-of-the-art performance on all testing datasets and shows superior model scalability. Results on two additional datasets reveals Hermes' strong performance for transfer learning incremental learning and generalization to downstream tasks. Hermes's learned priors demonstrate an appealing trait to reflect the intricate relations among tasks and modalities which aligns with the established anatomical and imaging principles in radiology. The code is available.

Annotating lots of 3D medical images for training segmentation models is time-consuming. The goal of weakly supervised semantic segmentation is to train segmentation models without using any ground truth segmentation masks. Our work addresses the case where only image-level categorical labels indicating the presence or absence of a particular region of interest (such as tumours or lesions) are available. Most existing methods rely on class activation mapping (CAM). We propose a novel approach ToNNO which is based on the Tomographic reconstruction of a Neural Network's Output. Our technique extracts stacks of slices with different angles from the input 3D volume feeds these slices to a 2D encoder and applies the inverse Radon transform in order to reconstruct a 3D heatmap of the encoder's predictions. This generic method allows to perform dense prediction tasks on 3D volumes using any 2D image encoder. We apply it to weakly supervised medical image segmentation by training the 2D encoder to output high values for slices containing the regions of interest. We test it on four large scale medical image datasets and outperform 2D CAM methods. We then extend ToNNO by combining tomographic reconstruction with CAM methods proposing Averaged CAM and Tomographic CAM which obtain even better results.

Federated learning facilitates the collaborative learning of a global model across multiple distributed medical institutions without centralizing data. Nevertheless the expensive cost of annotation on local clients remains an obstacle to effectively utilizing local data. To mitigate this issue federated active learning methods suggest leveraging local and global model predictions to select a relatively small amount of informative local data for annotation. However existing methods mainly focus on all local data sampled from the same domain making them unreliable in realistic medical scenarios with domain shifts among different clients. In this paper we make the first attempt to assess the informativeness of local data derived from diverse domains and propose a novel methodology termed Federated Evidential Active Learning (FEAL) to calibrate the data evaluation under domain shift. Specifically we introduce a Dirichlet prior distribution in both local and global models to treat the prediction as a distribution over the probability simplex and capture both aleatoric and epistemic uncertainties by using the Dirichlet-based evidential model. Then we employ the epistemic uncertainty to calibrate the aleatoric uncertainty. Afterward we design a diversity relaxation strategy to reduce data redundancy and maintain data diversity. Extensive experiments and analysis on five real multi-center medical image datasets demonstrate the superiority of FEAL over the state-of-the-art active learning methods in federated scenarios with domain shifts. The code will be available at https://github.com/JiayiChen815/FEAL.

Deep unfolding networks (DUN) have emerged as a popular iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction. However conventional DUN aims to reconstruct all the missing information within the entire space in each iteration. Thus it could be challenging when dealing with highly ill-posed degradation often resulting in subpar reconstruction. In this work we propose a Progressive Divide-And-Conquer (PDAC) strategy aiming to break down the subsampling process in the actual severe degradation and thus perform reconstruction sequentially. Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI we present a rigorous derivation of the proposed PDAC framework which could be further unfolded into an end-to-end trainable network. Each PDAC iteration specifically targets a distinct segment of moderate degradation based on the decomposition. Furthermore as part of the PDAC iteration such decomposition is adaptively learned as an auxiliary task through a degradation predictor which provides an estimation of the decomposed sampling mask. Following this prediction the sampling mask is further integrated via a severity conditioning module to ensure awareness of the degradation severity at each stage. Extensive experiments demonstrate that our proposed method achieves superior performance on the publicly available fastMRI and Stanford2D FSE datasets in both multi-coil and single-coil settings.

Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples. However their widespread use has also brought forth new challenges in model security which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We propose CAAT a simple but generic and efficient approach that does not require costly training to effectively fool latent diffusion models (LDMs). The approach is based on the observation that cross-attention layers exhibits higher sensitivity to gradient change allowing for leveraging subtle perturbations on published images to significantly corrupt the generated images. We show that a subtle perturbation on an image can significantly impact the cross-attention layers thus changing the mapping between text and image during the fine-tuning of customized diffusion models. Extensive experiments demonstrate that CAAT is compatible with diverse diffusion models and outperforms baseline attack methods in a more effective (more noise) and efficient (twice as fast as Anti-DreamBooth and Mist) manner.

Passive compact single-shot 3D sensing is useful in many application areas such as microscopy medical imaging surgical navigation and autonomous driving where form factor time and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance in an ultra-compact form factor and in a passive snapshot manner is challenging. Dual-pixel (DP) sensors are a potential solution to achieve the same. DP sensors collect light rays from two different halves of the lens in two interleaved pixel arrays thus capturing two slightly different views of the scene like a stereo camera system. However imaging with a DP sensor implies that the defocus blur size is directly proportional to the disparity seen between the views. This creates a trade-off between disparity estimation vs. deblurring accuracy. To improve this trade-off effect we propose CADS (Coded Aperture Dual-Pixel Sensing) in which we use a coded aperture in the imaging lens along with a DP sensor. In our approach we jointly learn an optimal coded pattern and the reconstruction algorithm in an end-to-end optimization setting. Our resulting CADS imaging system demonstrates improvement of >1.5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings. Furthermore we build the proposed CADS prototypes for DSLR photography settings and in an endoscope and a dermoscope form factor. Our novel coded dual-pixel sensing approach demonstrates accurate RGB-D reconstruction results in simulations and real-world experiments in a passive snapshot and compact manner.

Depression Recognition (DR) poses a considerable challenge especially in the context of the growing concerns surrounding privacy. Traditional automatic diagnosis of DR technology necessitates the use of facial images undoubtedly expose the patient identity features and poses privacy risks. In order to mitigate the potential risks associated with the inappropriate disclosure of patient facial images we design a new imaging system to erase the identity information of captured facial images while retain disease-relevant features. It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR. More specifically we try to record a de-identified facial image (erasing the identifiable features as much as possible) by a learnable lens which is optimized in conjunction with the following DR task as well as a range of face analysis related auxiliary tasks in an end-to-end manner. These aforementioned strategies form our final Optical deep Depression Recognition network (OpticalDR). Experiments on CelebA AVEC 2013 and AVEC 2014 datasets demonstrate that our OpticalDR has achieved state-of-the-art privacy protection performance with an average AUC of 0.51 on popular facial recognition models and competitive results for DR with MAE/RMSE of 7.53/8.48 on AVEC 2013 and 7.89/8.82 on AVEC 2014 respectively. Code is available at https://github.com/divertingPan/OpticalDR.

Large foundation models known for their strong zero-shot generalization have excelled in visual and language applications. However applying them to medical image segmentation a domain with diverse imaging types and target labels remains an open challenge. Current approaches such as adapting interactive segmentation models like Segment Anything Model (SAM) require user prompts for each sample during inference. Alternatively transfer learning methods like few/one-shot models demand labeled samples leading to high costs. This paper introduces a new paradigm toward the universal medical image segmentation termed 'One-Prompt Segmentation.' One-Prompt Segmentation combines the strengths of one-shot and interactive methods. In the inference stage with just one prompted sample it can adeptly handle the unseen task in a single forward pass. We train One-Prompt Model on 64 open-source medical datasets accompanied by the collection of over 3000 clinician-labeled prompts. Tested on 14 previously unseen datasets the One-Prompt Model showcases superior zero-shot segmentation capabilities outperforming a wide range of related methods. The code and data is released as https://github.com/KidsWithTokens/one-prompt.

Generalizability in deep neural networks plays a pivotal role in medical image segmentation. However deep learning-based medical image analyses tend to overlook the importance of frequency variance which is critical element for achieving a model that is both modality-agnostic and domain-generalizable. Additionally various models fail to account for the potential information loss that can arise from multi-task learning under deep supervision a factor that can impair the model's representation ability. To address these challenges we propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation which comprises two key components: a Multi-Frequency in Multi-Scale Attention (MFMSA) block and Ensemble Sub-Decoding Module (E-SDM). The MFMSA block refines the process of spatial feature extraction particularly in capturing boundary features by incorporating multi-frequency and multi-scale features thereby offering informative cues for tissue outline and anatomical structures. Moreover we propose E-SDM to mitigate information loss in multi-task learning with deep supervision especially during substantial upsampling from low resolution. We evaluate the segmentation performance of MADGNet across six modalities and fifteen datasets. Through extensive experiments we demonstrate that MADGNet consistently outperforms state-of-the-art models across various modalities showcasing superior segmentation performance. This affirms MADGNet as a robust solution for medical image segmentation that excels in diverse imaging scenarios. Our MADGNet code is available in GitHub Link.

Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-guided radiotherapy. Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations. However the former is sensitive to locally varying noise while the latter is not discriminative enough to cope with complex anatomical structures in multimodal scans causing ambiguity in determining the anatomical correspondence across scans with different modalities. In this paper we propose a modality-agnostic structural representation learning method which leverages Deep Neighbourhood Self-similarity (DNS) and anatomy-aware contrastive learning to learn discriminative and contrast-invariance deep structural image representations (DSIR) without the need for anatomical delineations or pre-aligned training images. We evaluate our method on multiphase CT abdomen MR-CT and brain MR T1w-T2w registration. Comprehensive results demonstrate that our method is superior to the conventional local structural representation and statistical-based similarity measures in terms of discriminability and accuracy.

The scarcity of annotated data has sparked significant interest in unsupervised pre-training methods that leverage medical reports as auxiliary signals for medical visual representation learning. However existing research overlooks the multi-granularity nature of medical visual representation and lacks suitable contrastive learning techniques to improve the models' generalizability across different granularities leading to the underutilization of image-text information. To address this we propose MLIP a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder local token-knowledge-patch alignment contrastive learning and knowledge-guided category-level contrastive learning with expert knowledge. Experimental evaluations reveal the efficacy of our model in enhancing transfer performance for tasks such as image classification object detection and semantic segmentation. Notably MLIP surpasses state-of-the-art methods even with limited annotated data highlighting the potential of multimodal pre-training in advancing medical representation learning.

Robust segmentation is critical for deriving quantitative measures from large-scale multi-center and longitudinal medical scans. Manually annotating medical scans however is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg) a unified UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation. To the best of our knowledge this is the first study that systematically reviews and develops a framework to tackle four different domain shifts in medical image segmentation. More importantly MAPSeg is the first framework that can be applied to centralized federated and test-time UDA while maintaining comparable performance. We compare MAPSeg with previous state-of-the-art methods on a private infant brain MRI dataset and a public cardiac CT-MRI dataset and MAPSeg outperforms others by a large margin (10.5 Dice improvement on the private MRI dataset and 5.7 on the public CT-MRI dataset). MAPSeg poses great practical value and can be applied to real-world problems. GitHub: https://github.com/XuzheZ/MAPSeg/.

Human pose and shape (HPS) estimation with lensless imaging is not only beneficial to privacy protection but also can be used in covert surveillance scenarios due to the small size and simple structure of this device. However this task presents significant challenges due to the inherent ambiguity of the captured measurements and lacks effective methods for directly estimating human pose and shape from lensless data. In this paper we propose the first end-to-end framework to recover 3D human poses and shapes from lensless measurements to our knowledge. We specifically design a multi-scale lensless feature decoder to decode the lensless measurements through the optically encoded mask for efficient feature extraction. We also propose a double-head auxiliary supervision mechanism to improve the estimation accuracy of human limb ends. Besides we establish a lensless imaging system and verify the effectiveness of our method on various datasets acquired by our lensless imaging system. The code and dataset are available at https://cic.tju.edu.cn/faculty/likun/projects/LPSNet.

Dual-camera compressive hyperspectral imaging (DCCHI) offers the capability to reconstruct 3D hyperspectral image (HSI) by fusing compressive and panchromatic (PAN) image which has shown great potential for snapshot hyperspectral imaging in practice. In this paper we introduce a novel DCCHI reconstruction network intra-inter similarity exploiting Transformer (In2SET). Our key insight is to make full use of the PAN image to assist the reconstruction. To this end we propose to use the intra-similarity within the PAN image as a proxy for approximating the intra-similarity in the original HSI thereby offering an enhanced content prior for more accurate HSI reconstruction. Furthermore we propose to use the inter-similarity to align the features between HSI and PAN images thereby maintaining semantic consistency between the two modalities during the reconstruction process. By integrating In2SET into a PAN-guided deep unrolling (PGDU) framework our method substantially enhances the spatial-spectral fidelity and detail of the reconstructed images providing a more comprehensive and accurate depiction of the scene. Experiments conducted on both real and simulated datasets demonstrate that our approach consistently outperforms existing state-of-the-art methods in terms of reconstruction quality and computational complexity. The code is available at https://github.com/2JONAS/In2SET.

The astonishing development of single-photon cameras has created an unprecedented opportunity for scientific and industrial imaging. However the high data throughput generated by these 1-bit sensors creates a significant bottleneck for low-power applications. In this paper we explore the possibility of generating a color image from a single binary frame of a single-photon camera. We evidently find this problem being particularly difficult to standard colorization approaches due to the substantial degree of exposure variation. The core innovation of our paper is an exposure synthesis model framed under a neural ordinary differential equation (Neural ODE) that allows us to generate a continuum of exposures from a single observation. This innovation ensures consistent exposure in binary images that colorizers take on resulting in notably enhanced colorization. We demonstrate applications of the method in single-image and burst colorization and show superior generative performance over baselines. Project website can be found at https://vishal-s-p.github.io/projects/2023/generative_quanta_color.html