GenAI4ELab/papercli-papers huggingface dataset:
BA-Net: Bridge Attention for Deep Convolutional Neural Networks
Authors: Yue Zhao; Junzhou Chen; Zirui Zhang; Ronghui Zhang
Venue: ECCV 2022 (main)

Abstract: "In attention mechanism research, most existing methods are hard to utilize well the information of the neural network with high computing efficiency due to heavy feature compression in the attention layer. This paper proposes a simple and general approach named Bridge Attention to address this issue. As a new idea, BA-Net straightforwardly integrates features from previous layers and effectively promotes information interchange. Only simple strategies are employed for the model implementation, similar to the SENet. Moreover, after extensively investigating the effectiveness of different previous features, we discovered a simple and exciting insight that bridging all the convolution outputs inside each block with BN can obtain better attention to enhance the performance of neural networks. BA-Net is effective, stable, and easy to use. A comprehensive evaluation of computer vision tasks demonstrates that the proposed approach achieves better performance than the existing channel attention methods regarding accuracy and computing efficiency."
SAU: Smooth Activation Function Using Convolution with Approximate Identities
Authors: Koushik Biswas; Sandeep Kumar; Shilpak Banerjee; Ashish Kumar Pandey
Venue: ECCV 2022 (main)

Abstract: "Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin. Over the years, many smooth approximations of ReLU have been proposed using various smoothing techniques. We propose new smooth approximations of a non-differentiable activation function by convolving it with approximate identities. In particular, we present smooth approximations of Leaky ReLU and show that they outperform several well-known activation functions in various datasets and models. We call this function Smooth Activation Unit (SAU). Replacing ReLU by SAU, we get 5.63%, 2.95%, and 2.50% improvement with ShuffleNet V2 (2.0x), PreActResNet 50 and ResNet 50 models respectively on the CIFAR100 dataset and 2.31% improvement with ShuffleNet V2 (1.0x) model on ImageNet-1k dataset."
Multi-Exit Semantic Segmentation Networks
Authors: Alexandros Kouris; Stylianos I. Venieris; Stefanos Laskaridis; Nicholas Lane
Venue: ECCV 2022 (main)

Abstract: "Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently operating under stringent latency constraints within a limited resource envelope, optimising for efficient execution becomes important. At the same time, the heterogeneous capabilities of the target platforms and diverse constraints of different applications require the design and training of multiple target-specific segmentation models, leading to excessive maintenance costs. To this end, we propose a framework for converting state-of-the-art segmentation CNNs to Multi-Exit Semantic Segmentation (MESS) networks: specially trained models that employ parametrised early exits along their depth to i) dynamically save computation during inference on easier samples and ii) save training and maintenance cost by offering a post-training customisable speed-accuracy trade-off. Designing and training such networks naively can hurt performance. Thus, we propose novel two-staged training scheme for multi-exit networks. Furthermore, the parametrisation of MESS enables co-optimising the number, placement and architecture of the attached segmentation heads along with the exit policy, upon deployment via exhaustive search in <1GPUh. This allows MESS to rapidly adapt to the device capabilities and application requirements for each target use-case, offering a train-once-deploy-everywhere solution. MESS variants achieve latency gains of up to 2.83x with the same accuracy, or 5.33 pp higher accuracy for the same computational budget, compared to the original backbone network. Lastly, MESS delivers orders of magnitude faster architectural customisation, compared to state-of-the-art techniques."
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks
Authors: Bernd Prach; Christoph H. Lampert
Venue: ECCV 2022 (main)

Abstract: "It is a highly desirable property for deep networks to be robust against small input changes. One popular way to achieve this property is by designing networks with a small Lipschitz constant. In this work, we propose a new technique for constructing such Lipschitz networks that has a number of desirable properties: it can be applied to any linear network layer (fully-connected or convolutional), it provides formal guarantees on the Lipschitz constant, it is easy to implement and efficient to run, and it can be combined with any training objective and optimization method. In fact, our technique is the first one in the literature that achieves all of these properties simultaneously. Our main contribution is a rescaling-based weight matrix parametrization that guarantees each network layer to have a Lipschitz constant of at most 1 and results in the learned weight matrices to be close to orthogonal. Hence we call such layers almost-orthogonal Lipschitz (AOL). Experiments and ablation studies in the context of image classification with certified robust accuracy confirm that AOL layers achieve results that are on par with most existing methods. Yet, they are simpler to implement and more broadly applicable, because they do not require computationally expensive matrix orthogonalization or inversion steps as part of the network architecture. We provide code at https://github.com/berndprach/AOL."
PointScatter: Point Set Representation for Tubular Structure Extraction
Authors: Dong Wang; Zhao Zhang; Ziwei Zhao; Yuhang Liu; Yihong Chen; Liwei Wang
Venue: ECCV 2022 (main)

Abstract: "This paper explores the point set representation for tubular structure extraction tasks. Compared with the traditional mask representation, the point set representation enjoys its flexibility and representation ability, which would not be restricted by the fixed grid as the mask. Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task. PointScatter splits the image into scatter regions and parallelly predicts points for each scatter region. We further propose the greedy-based region-wise bipartite matching algorithm to train the network end-to-end and efficiently. We benchmark the PointScatter on four public tubular datasets, and the extensive experiments on tubular structure segmentation and centerline extraction task demonstrate the effectiveness of our approach. Code is available at https://github.com/zhangzhao2022/pointscatter."
Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection
Authors: Ziwei Zhao; Dong Wang; Yihong Chen; Ziteng Wang; Liwei Wang
Venue: ECCV 2022 (main)

Abstract: "Detecting mass in mammogram is significant due to the high occurrence and mortality of breast cancer. In mammogram mass detection, modeling pairwise lesion correspondence explicitly is particularly important. However, most of the existing methods build relatively coarse correspondence and have not utilized correspondence supervision. In this paper, we propose a new transformer-based framework CL-Net to learn lesion detection and pairwise correspondence in an end-to-end manner. In CL-Net, View-Interactive Lesion Detector is proposed to achieve dynamic interaction across candidates of cross views, while Lesion Linker employs the correspondence supervision to guide the interaction process more accurately. The combination of these two designs accomplishes precise understanding of pairwise lesion correspondence for mammograms. Experiments show that CL-Net yields state-of-the-art performance on the public DDSM dataset and our in-house dataset. Moreover, it outperforms previous methods by a large margin in low FPI regime."
Graph-Constrained Contrastive Regularization for Semi-Weakly Volumetric Segmentation
Authors: Simon Reiß; Constantin Seibold; Alexander Freytag; Erik Rodner; Rainer Stiefelhagen
Venue: ECCV 2022 (main)

Abstract: "Semantic volume segmentation suffers from the requirement of having voxel-wise annotated ground-truth data, which requires immense effort to obtain. In this work, we investigate how models can be trained from sparsely annotated volumes, i.e. volumes with only individual slices annotated. By formulating the scenario as a semi-weakly supervised problem where only some regions in the volume are annotated, we obtain surprising results: expensive dense volumetric annotations can be replaced by cheap, partially labeled volumes with limited impact on accuracy if the hypothesis space of valid models gets properly constrained during training. With our Contrastive Constrained Regularization (Con2R), we demonstrate that 3D convolutional models can be trained with less than 4% of only two dimensional ground-truth labels and still reach up to 88% accuracy of fully supervised baseline models with dense volumetric annotations. To get insights into Con2Rs success, we study how strong semi-supervised algorithms transfer to our new volumetric semi-weakly supervised setting. In this manner, we explore retinal fluid and brain tumor segmentation and give a detailed look into accuracy progression for scenarios with extremely scarce labels."
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration
Authors: Ziqi Zhou; Lei Qi; Yinghuan Shi
Venue: ECCV 2022 (main)

Abstract: "For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularization. We also design a random amplitude mixup (RAM) module, which incorporates low-level frequency information of different domain images to synthesize new images. To guide our model be resistant to domain shift, we introduce a semantic consistency loss. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance."
Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation
Authors: Pengfei Guo; Dong Yang; Ali Hatamizadeh; An Xu; Ziyue Xu; Wenqi Li; Can Zhao; Daguang Xu; Stephanie Harmon; Evrim Turkbey; Baris Turkbey; Bradford Wood; Francesca Patella; Elvira Stellato; Gianpaolo Carrafiello; Vishal M. Patel; Holger R. Roth
Venue: ECCV 2022 (main)

Abstract: "Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordable with limited compute budgets. In this work, we propose an efficient reinforcement learning (RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL, in which an online RL agent can dynamically adjust hyperparameters of each client based on the current training progress. Extensive experiments are conducted to investigate different search strategies and RL agents. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset as well as two real-world medical image segmentation datasets for COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT.."
Personalizing Federated Medical Image Segmentation via Local Calibration
Authors: Jiacheng Wang; Yueming Jin; Liansheng Wang
Venue: ECCV 2022 (main)

Abstract: "Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing datasets. However, using a single model to adapt to various data distributions from different sites is extremely challenging. Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site. However, most existing methods concentrate on the partial parameter splitting, while do not consider the inter-site in-consistencies during the local training, which in fact can facilitate the knowledge communication over sites to benefit the model learning for improving the local accuracy. In this paper, we propose a personalized federated framework with Local Calibration (LC-Fed), to leverage the inter-site in-consistencies in both feature- and prediction- levels to boost the segmentation. Concretely, as each local site has its alternative attention on the various features, we first design the contrastive site embedding coupled with channel selection operation to calibrate the encoded features. Moreover, we propose to exploit the knowledge of prediction-level in-consistency to guide the personalized modeling on the ambiguous regions, e.g., anatomical boundaries. It is achieved by computing a disagreement-aware map to calibrate the prediction. Effectiveness of our method has been verified on three medical image segmentation tasks with different modalities, where our method consistently shows superior performance to the state-of-the-art personalized FL methods. Code is available at https://github.com/jcwang123/FedLC."
One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement
Authors: Zihao Yin; Ping Gong; Chunyu Wang; Yizhou Yu; Yizhou Wang
Venue: ECCV 2022 (main)

Abstract: "As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance. Besides, due to cumbersome collection procedures, the limited size of medical landmark datasets impacts the effectiveness of large-scale self-supervised pre-training methods. To address these challenges, we propose a two-stage framework for one-shot medical landmark localization, which first infers landmarks by unsupervised registration from the labeled exemplar to unlabeled targets, and then utilizes these noisy pseudo labels to train robust detectors. To handle the significant structure variations, we learn an end-to-end cascade of global alignment and local deformations, under the guidance of novel loss functions which incorporate edge information. In stage \uppercase\expandafter{\romannumeral2}, we explore self-consistency for selecting reliable pseudo labels and cross-consistency for semi-supervised learning. Our method achieves state-of-the-art performances on public datasets of different body parts, which demonstrates its general applicability. Code will be publicly available."
Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization
Authors: Ming-Yang Ho; Min-Sheng Wu; Che-Ming Wu
Venue: ECCV 2022 (main)

Abstract: "While hematoxylin and eosin (H&E) is a standard staining procedure, immunohistochemistry (IHC) staining further serves as a diagnostic and prognostic method. However, acquiring special staining results requires substantial costs. Hence, we proposed a strategy for ultra-high-resolution unpaired image-to-image translation: Kernelized Instance Normalization (KIN), which preserves local information and successfully achieves seamless stain transformation with constant GPU memory usage. Given a patch, corresponding position, and a kernel, KIN computes local statistics using convolution operation. In addition, KIN can be easily plugged into most currently developed frameworks without re-training. We demonstrate that KIN achieves state-of-the-art stain transformation by replacing instance normalization (IN) layers with KIN layers in three popular frameworks and testing on two histopathological datasets. Furthermore, we manifest the generalizability of KIN with high-resolution natural images. Finally, human evaluation and several objective metrics are used to compare the performance of different approaches. Overall, this is the first successful study for the ultra-high-resolution unpaired image-to-image translation with constant space complexity. Code is available at: https://github.com/Kaminyou/URUST."
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation
Authors: Wenxuan Wang; Chen Chen; Jing Wang; Sen Zha; Yan Zhang; Jiangyun Li
Venue: ECCV 2022 (main)

Abstract: "For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network to dynamically select a suitable model from the predefined Model Bank for the subsequent 2D segmentation task. Extensive experimental results on both BraTS 2019 and 2020 datasets show that our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation with much less model complexity. Compared with the state-of-the-art 3D method TransBTS, the proposed framework improves the model efficiency by up to 3.5x without sacrificing the accuracy. Our code will be publicly available at https://github.com/Wenxuan-1119/Med-DANet."
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images
Authors: Jiawei Yang; Hanbo Chen; Yuan Liang; Junzhou Huang; Lei He; Jianhua Yao
Venue: ECCV 2022 (main)

Abstract: "Detecting and segmenting objects within whole slide images is essential in computational pathology workflow. Self-supervised learning (SSL) is appealing to such annotation-heavy tasks. Despite the extensive benchmarks in natural images for dense tasks, such studies are, unfortunately, absent in current works for pathology. Our paper in- tends to narrow this gap. We first benchmark representative SSL methods for dense prediction tasks in pathology images. Then, we propose concept contrastive learning (ConCL), an SSL framework for dense pre-training. We explore how ConCL performs with concepts provided by different sources and end up with proposing a simple dependency-free concept generating method that does not rely on external segmentation algorithms or saliency detection models. Extensive experiments demonstrate the superiority of ConCL over previous state-of-the-art SSL methods across different settings. Along our exploration, we distill several important and intriguing components contributing to the success of dense pre-training for pathology images. We hope this work could provide useful data points and encourage the community to conduct ConCL pre-training for problems of interest. Code is available."
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images
Authors: Axel Levy; Frédéric Poitevin; Julien Martel; Youssef Nashed; Ariana Peck; Nina Miolane; Daniel Ratner; Mike Dunne; Gordon Wetzstein
Venue: ECCV 2022 (main)

Abstract: "Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetrized loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods."
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier
Authors: Yutong Xie; Jianpeng Zhang; Yong Xia; Qi Wu
Venue: ECCV 2022 (main)

Abstract: "Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D medical images like computerized tomography (CT) remains challenging due to its high imaging cost and privacy restrictions. In this paper, we advocate bringing a wealth of 2D images like chest X-rays as compensation for the lack of 3D data, aiming to build a universal medical self-supervised representation learning framework, called UniMiSS. The following problem is how to break the dimensionality barrier, i.e., making it possible to perform SSL with both 2D and 3D images? To achieve this, we design a pyramid U-like medical Transformer (MiT). It is composed of the switchable patch embedding (SPE) module and Transformers. The SPE module adaptively switches to either 2D or 3D patch embedding, depending on the input dimension. The embedded patches are converted into a sequence regardless of their original dimensions. The Transformers model the long-term dependencies in a sequence-to-sequence manner, thus enabling UniMiSS to learn representations from both 2D and 3D images. With the MiT as the backbone, we perform the UniMiSS in a self-distillation manner. We conduct expensive experiments on six 3D/2D medical image analysis tasks, including segmentation and classification. The results show that the proposed UniMiSS achieves promising performance on various downstream tasks, outperforming the ImageNet pre-training and other advanced SSL counterparts substantially. Code is available at https://github.com/YtongXie/UniMiSS-code."