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Ambiguous Medical Image Segmentation using Diffusion Models

Ambiguous Medical Image Segmentation using Diffusion Models

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rahman, Aimon Valanarasu, Jeya Maria Jose Hacihaliloglu, Ilker Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code: https://***/aimansnigdha/Ambiguous-Medical-Image-Segmentation-using-Diffusion-Models.

关键词： cell microscopy Medical and biological vision

来源：评论

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MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection

MetaFusion: Infrared and Visible Image Fusion via Meta-Featu...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Wenda Xie, Shigeng Zhao, Fan He, You Lu, Huchuan Dalian Univ Technol Dalian Peoples R China Liaoning Normal Univ Dalian Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more attention. However, the feature gap between these two different-level tasks hinders the progress. Addressing this issue, this paper proposes an infrared and visible image fusion via meta-feature embedding from object detection. The core idea is that meta-feature embedding model is designed to generate object semantic features according to fusion network ability, and thus the semantic features are naturally compatible with fusion features. It is optimized by simulating a meta learning. Moreover, we further implement a mutual promotion learning between fusion and detection tasks to improve their performances. Comprehensive experiments on three public datasets demonstrate the effectiveness of our method. Code and model are available at: https://***/wdzhao123/MetaFusion.

关键词： Low-level vision

来源：评论

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Probabilistic Prompt Learning for Dense Prediction

Probabilistic Prompt Learning for Dense Prediction

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kwon, Hyeongjun Song, Taeyong Jeong, Somi Kim, Jin Jang, Jinhyun Sohn, Kwanghoon Yonsei Univ Seoul South Korea Hyundai Motor Co R&D Div Seoul South Korea NAVER LABS Seongnam South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9798350301298

Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approach results in limited performance for dense prediction tasks that require handling more complex and diverse objects, since a single and deterministic description cannot sufficiently represent the entire image. In this paper, we present a novel probabilistic prompt learning to fully exploit the vision-language knowledge in dense prediction tasks. First, we introduce learnable class-agnostic attribute prompts to describe universal attributes across the object class. The attributes are combined with class information and visual-context knowledge to define the class-specific textual distribution. Text representations are sampled and used to guide the dense prediction task using the probabilistic pixel-text matching loss, enhancing the stability and generalization capability of the proposed method. Extensive experiments on different dense prediction tasks and ablation studies demonstrate the effectiveness of our proposed method.

关键词： and reasoning language vision

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SketchXAI: A First Look at Explainability for Human Sketches

SketchXAI: A First Look at Explainability for Human Sketches

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qu, Zhiyu Gryaditskayal, Yulia Li, Ke Pang, Kaiyue Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford Surrey England Beijing Univ Posts & Telecommun Beijing Peoples R China IFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England

ISBN: (纸本)9798350301298

This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a "human-centred" data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by identifying strokes as a unique building block that offers a degree of flexibility in object construction and manipulation impossible in photos. Following this, we design a simple explainability-friendly sketch encoder that accommodates the intrinsic properties of strokes: shape, location, and order. We then move on to define the first ever XAI task for sketch, that of stroke location inversion (SLI). Just as we have heat maps for photos, and correlation matrices for text, SLI offers an explainability angle to sketch in terms of asking a network how well it can recover stroke locations of an unseen sketch. We offer qualitative results for readers to interpret as snapshots of the SLI process in the paper, and as GIFs on the project page. A minor but interesting note is that thanks to its sketch-specific design, our sketch encoder also yields the best sketch recognition accuracy to date while having the smallest number of parameters. The code is available at https://***.

关键词： Explainable computer vision

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SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries

SplineCam: Exact Visualization and Characterization of Deep ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Humayun, Ahmed Imtiaz Balestriero, Randall Ralakrishnan, Ouha Raraniuk, Richard Rice Univ Houston TX 77005 USA Meta AI FAIR New York NY USA

ISBN: (纸本)9798350301298

Current Deep Network (DN) visualization and interpretability methods rely heavily on data space visualizations such as scoring which dimensions of the data are responsible for their associated prediction or generating new data features or samples that best match a given DN unit or representation. In this paper, we go one step further by developing the first provably exact method for computing the geometry of a DN's mapping - including its decision boundary - over a specified region of the data space. By leveraging the theory of Continuous Piece-Wise Linear (CPWL) spline DNs, SplineCam exactly computes a DN's geometry without resorting to approximations such as sampling or architecture simplification. SplineCam applies to any DN architecture based on CPWL activation nonlinearities, including (leaky) ReLU, absolute value, maxout, and max-pooling and can also be applied to regression DNs such as implicit neural representations. Beyond decision boundary visualization and characterization, SplineCam enables one to compare architectures, measure generalizability, and sample from the decision boundary on or off the data manifold. Project website: ***/splinecam.

关键词： computer vision theory

来源：评论

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Neumann Network with Recursive Kernels for Single Image Defocus Deblurring

Neumann Network with Recursive Kernels for Single Image Defo...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Quan, Yuhui Wu, Zicong Ji, Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (纸本)9798350301298

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the blob-type structure of defocus kernels, we propose a learnable recursive kernel representation (RKR) for defocus kernels that expresses a defocus kernel by a linear combination of recursive, separable and positive atom kernels, leading to a compact yet effective and physics-encoded parametrization of the spatially-varying defocus blurring process. Afterwards, a physics-driven and efficient deep model with a cross-scale fusion structure is presented for SIDD, with inspirations from the truncated Neumann series for approximating the matrix inversion of the RKR-based blurring operator. In addition, a reblurring loss is proposed to regularize the RKR learning. Extensive experiments show that, our proposed approach significantly outperforms existing ones, with a model size comparable to that of the top methods.

关键词： Low-level vision

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Toward Accurate Post-Training Quantization for Image Super Resolution

Toward Accurate Post-Training Quantization for Image Super R...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tu, Zhijun Hu, Jie Chen, Hanting Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Model quantization is a crucial step for deploying super resolution (SR) networks on mobile devices. However, existing works focus on quantization-aware training, which requires complete dataset and expensive computational overhead. In this paper, we study post-training quantization (PTQ) for image super resolution using only a few unlabeled calibration images. As the SR model aims to maintain the texture and color information of input images, the distribution of activations are long-tailed, asymmetric and highly dynamic compared with classification models. To this end, we introduce the density-based dual clipping to cut off the outliers based on analyzing the asymmetric bounds of activations. Moreover, we present a novel pixel aware calibration method with the supervision of the full-precision model to accommodate the highly dynamic range of different samples. Extensive experiments demonstrate that the proposed method significantly outperforms existing PTQ algorithms on various models and datasets. For instance, we get a 2.091 dB increase on Urban100 benchmark when quantizing EDSRx4 to 4-bit with 100 unlabeled images. Our code is available at both PyTorch and MindSpore.

关键词： Low-level vision

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Pseudo-label Guided Contrastive Learning for Semi-supervised Medical Image Segmentation

Pseudo-label Guided Contrastive Learning for Semi-supervised...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Basak, Hritam Yin, Zhaozheng SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9798350301298

Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images. Contrastive Learning (CL) frameworks use the notion of similarity measure which is useful for classification problems, however, they fail to transfer these quality representations for accurate pixel-level segmentation. To this end, we propose a novel semi-supervised patch-based CL framework for medical image segmentation without using any explicit pretext task. We harness the power of both CL and SemiSL, where the pseudo-labels generated from SemiSL aid CL by providing additional guidance, whereas discriminative class information learned in CL leads to accurate multi-class segmentation. Additionally, we formulate a novel loss that synergistically encourages inter-class separability and intraclass compactness among the learned representations. A new inter-patch semantic disparity mapping using average patch entropy is employed for a guided sampling of positives and negatives in the proposed CL framework. Experimental analysis on three publicly available datasets of multiple modalities reveals the superiority of our proposed method as compared to the state-of-the-art methods. Code is available at: GitHub.

关键词： cell microscopy Medical and biological vision

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Multiplicative Fourier Level of Detail

Multiplicative Fourier Level of Detail

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dou, Yishun Zheng, Zhong Jin, Qiaoqiao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated by a sinusoidal function and then element-wisely multiplied by a linear transformation of previous layer's representation in a layer-to-layer recursive manner, yielding the scale-aggregated encodings for a subsequent simple linear forward to get final output. In contrast to previous hybrid representations relying on interleaved multilevel fusion and nonlinear activation-based decoding, MFLOD could be elegantly characterized as a linear combination of sine basis functions with varying amplitude, frequency, and phase upon the learned multilevel features, thus offering great feasibility in Fourier analysis. Comprehensive experimental results on implicit neural representation learning tasks including image fitting, 3D shape representation, and neural radiance fields well demonstrate the superior quality and generalizability achieved by the proposed MFLOD scheme.

关键词： vision + graphics

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Fast Point Cloud Generation with Straight Flows

Fast Point Cloud Generation with Straight Flows

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Lemeng Wang, Dilin Gong, Chengyue Liu, Xingchao Xiong, Yunyang Ranjan, Rakesh Krishnamoorthi, Raghuraman Chandra, Vikas Liu, Qiang Univ Texas Austin Austin TX 78712 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350301298

Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D real-world. To address this limitation, we propose Point Straight Flow (PSF), a model that exhibits impressive performance using one step. Our idea is based on the reformulation of the standard diffusion model, which optimizes the curvy learning trajectory into a straight path. Further, we develop a distillation strategy to shorten the straight path into one step without a performance loss, enabling applications to 3D real-world with latency constraints. We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods. On real-world applications such as point cloud completion and training-free text-guided generation in a low-latency setup, PSF performs favorably.

关键词： Efficient and scalable vision

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