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Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical image Datasets 1

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International Challenge on Segment Anything in Medical images on Laptop held in conjunction with the IEEE/CVF conference on computer vision and pattern recognition, CVPR 2024

作者： Gao, Ruochen Lyu, Donghang Staring, Marius Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands

ISBN: (数字)9783031818547

ISBN: (纸本)9783031818530

Medical imaging is essential for the diagnosis and treatment of diseases, with medical image segmentation as a subtask receiving high attention. However, automatic medical image segmentation models are typically task-specific and struggle to handle multiple scenarios, such as different imaging modalities and regions of interest. With the introduction of the Segment Anything Model (SAM), training a universal model for various clinical scenarios has become feasible. Recently, several Medical SAM (MedSAM) methods have been proposed, but these models often rely on heavy image encoders to achieve high performance, which may not be practical for real-world applications due to their high computational demands and slow inference speed. To address this issue, a lightweight version of the MedSAM (LiteMedSAM) can provide a viable solution, achieving high performance while requiring fewer resources and less time. In this work, we introduce Swin-LiteMedSAM, a new variant of LiteMedSAM. This model integrates the tiny Swin Transformer as the image encoder, incorporates multiple types of prompts, including box-based points and scribble generated from a given bounding box, and establishes skip connections between the image encoder and the mask decoder. In the Segment Anything in Medical images on Laptop challenge (CVPR 2024), our approach strikes a good balance between segmentation performance and speed, demonstrating significantly improved overall results across multiple modalities compared to the LiteMedSAM baseline provided by the challenge organizers. Our proposed model achieved a DSC score of 0.8678 and an NSD score of 0.8844 on the validation set. On the final test set, it attained a DSC score of 0.8193 and an NSD score of 0.8461, securing fourth place in the challenge. The code and trained model are available at https://***/RuochenGao/Swin_LiteMedSAM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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All-in-Focus Imaging from Event Focal Stack

All-in-Focus Imaging from Event Focal Stack

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conference on computer vision and pattern recognition (CVPR)

作者： Hanyue Lou Minggui Teng Yixin Yang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods.

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Learning Distortion Invariant Representation for image Restoration from a Causality Perspective

Learning Distortion Invariant Representation for Image Resto...

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conference on computer vision and pattern recognition (CVPR)

作者： Xin Li Bingchen Li Xin Jin Cuiling Lan Zhibo Chen University of Science and Technology of China Eastern Institute for Advanced Study Microsoft Research Asia

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtual model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the generalization capability of our DIL on unseen distortion types and degrees. Our code will be available at https://***/lixinustc/Causal-IR-DIL.

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Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping

Bootstrapping Objectness from Videos by Relaxed Common Fate ...

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conference on computer vision and pattern recognition (CVPR)

作者： Long Lian Zhirong Wu Stella X. Yu UC Berkeley Microsoft Research Asia University of Michigan

We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move at the same speed belong together, has inspired unsupervised object discovery based on motion segmentation. However, common fate is not a reliable indicator of objectness: Parts of an articulated / deformable object may not move at the same speed, whereas shadows / reflections of an object always move with it but are not part of it. Our insight is to bootstrap objectness by first learning image features from relaxed common fate and then refining them based on visual appearance grouping within the image itself and across images statistically. Specifically, we learn an image segmenter first in the loop of approximating optical flow with constant segment flow plus small within-segment residual flow, and then by refining it for more coherent appearance and statistical figure-ground relevance. On unsupervised video object segmentation, using only ResNet and convolutional heads, our model surpasses the state-of-the-art by absolute gains of 7/9/5% on DAVIS16 / STv2 / FBMS59 respectively, demonstrating the effectiveness of our ideas. Our code is publicly available.

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Human Spine Motion Capture using Perforated Kinesiology Tape

Human Spine Motion Capture using Perforated Kinesiology Tape

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IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hendrik Hachmann Bodo Rosenhahn Institute for Information Processing (tnt) / L3S Leibniz University Hannover

In this work, we present a marker-based multi-view spine tracking method that is specifically adjusted to the requirements for movements in sports. A maximal focus is on the accurate detection of markers and fast usage of the system. For this task, we take advantage of the prior knowledge of the arrangement of dots in perforated kinesiology tape. We detect the tape and its dots using a Mask R-CNN and a blob detector. Here, we can focus on detection only while skipping any image-based feature encoding or matching. We conduct a reasoning in 3D by a linear program and Markov random fields, in which the structure of the kinesiology tape is modeled and the shape of the spine is optimized. In comparison to state-of-the-art systems, we demonstrate that our system achieves high precision and marker density, is robust against occlusions, and capable of capturing fast movements.

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L-CoIns: Language-based Colorization With Instance Awareness

L-CoIns: Language-based Colorization With Instance Awareness

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conference on computer vision and pattern recognition (CVPR)

作者： Zheng Chang Shuchen Weng Peixuan Zhang Yu Li Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University International Digital Economy Academy

Language-based colorization produces plausible colors consistent with the language description provided by the user. Recent studies introduce additional annotation to prevent color-object coupling and mismatch issues, but they still have difficulty in distinguishing instances corresponding to the same object words. In this paper, we propose a transformer-based framework to automatically aggregate similar image patches and achieve instance awareness without any additional knowledge. By applying our presented luminance augmentation and counter-color loss to break down the statistical correlation between luminance and color words, our model is driven to synthesize colors with better descriptive consistency. We further collect a dataset to provide distinctive visual characteristics and detailed language descriptions for multiple instances in the same image. Extensive experiments demonstrate our advantages of synthesizing visually pleasing and description-consistent results of instance-aware colorization.

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Application on the Loop of Multimodal image Fusion: Trends on Deep-Learning Based Approaches

Application on the Loop of Multimodal Image Fusion: Trends o...

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pattern recognition Systems (ICPRS), International conference on

作者： Gisel Bastidas-Guacho Patricio Moreno-Vallejo Boris Vintimilla Angel D. Sappa ESPOL Polytechnic University Ecuador ESPOCH Polytechnic University Ecuador Computer Vision Center Spain

Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. There are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. Then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion.

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Learning to Exploit Temporal Structure for Biomedical vision-Language processing

Learning to Exploit Temporal Structure for Biomedical Vision...

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conference on computer vision and pattern recognition (CVPR)

作者： Shruthi Bannur Stephanie Hyland Qianchu Liu Fernando Pérez-García Maximilian Ilse Daniel C. Castro Benedikt Boecking Harshita Sharma Kenza Bouzid Anja Thieme Anton Schwaighofer Maria Wetscherek Matthew P. Lungren Aditya Nori Javier Alvarez-Valle Ozan Oktay Microsoft Health Futures

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

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Streaming Video Model

Streaming Video Model

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conference on computer vision and pattern recognition (CVPR)

作者： Yucheng Zhao Chong Luo Chuanxin Tang Dongdong Chen Noel Codella Zheng-Jun Zha University of Science and Technology of China Microsoft Research Asia Microsoft Cloud + AI

Video understanding tasks have traditionally been modeled by two separate architectures, specially tailored for two distinct tasks. Sequence-based video tasks, such as action recognition, use a video backbone to directly extract spatiotemporal features, while frame-based video tasks, such as multiple object tracking (MOT), rely on single fixed-image backbone to extract spatial features. In contrast, we propose to unify video understanding tasks into one novel streaming video architecture, referred to as Streaming vision Transformer (S-ViT). S-ViT first produces frame-level features with a memory-enabled temporally-aware spatial encoder to serve the frame-based video tasks. Then the frame features are input into a task-related temporal decoder to obtain spatiotemporal features for sequence-based tasks. The efficiency and efficacy of S-ViT is demonstrated by the state-of-the-art accuracy in the sequence-based action recognition task and the competitive advantage over conventional architecture in the frame-based MOT task. We believe that the concept of streaming video model and the implementation of S-ViT are solid steps towards a unified deep learning architecture for video understanding. Code will be available at https://***/yuzhms/Streaming-Video-Model.

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image Super-Resolution Using T-Tetromino Pixels

Image Super-Resolution Using T-Tetromino Pixels

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conference on computer vision and pattern recognition (CVPR)

作者： Simon Grosche Andy Regensky Jürgen Seiler André Kaup Chair of Multimedia Communications and Signal Processing Friedrich-Alexander-Univeristät Erlangen-Nürnberg Erlangen Germany

For modern high-resolution imaging sensors, pixel binning is performed in low-lighting conditions and in case high frame rates are required. To recover the original spatial resolution, single-image super-resolution techniques can be applied for upscaling. To achieve a higher image quality after upscaling, we propose a novel binning concept using tetromino-shaped pixels. It is embedded into the field of compressed sensing and the coherence is calculated to motivate the sensor layouts used. Next, we investigate the reconstruction quality using tetromino pixels for the first time in literature. Instead of using different types of tetrominoes as proposed elsewhere, we show that using a small repeating cell consisting of only four T-tetrominoes is sufficient. For reconstruction, we use a locally fully connected reconstruction (LFCR) network as well as two classical reconstruction methods from the field of compressed sensing. Using the LFCR network in combination with the proposed tetromino layout, we achieve superior image quality in terms of PSNR, SSIM, and visually compared to conventional single-image super-resolution using the very deep super-resolution (VDSR) network. For PSNR, a gain of up to +1.92 dB is achieved.

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