pwc-archive/papers-with-abstracts huggingface dataset:
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
Authors: Georg Wölflein, Dyke Ferber, Asier R. Meneghetti, Omar S. M. El Nahhas, Daniel Truhn, Zunamys I. Carrero, David J. Harrison, Ognjen Arandjelović, Jakob Nikolas Kather
Date: 2023-11-20 00:00:00
Tasks: Benchmarking, image-classification, Image Classification, Self-Supervised Learning, Weakly-supervised Learning

Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting the slide. Constructing models to solve this task involves multiple design choices, often made without robust empirical or conclusive theoretical justification. To address this, we conduct a comprehensive benchmarking of feature extractors to answer three critical questions: 1) Is stain normalisation still a necessary preprocessing step? 2) Which feature extractors are best for downstream slide-level classification? 3) How does magnification affect downstream performance? Our study constitutes the most comprehensive evaluation of publicly available pathology feature extractors to date, involving more than 10,000 training runs across 14 feature extractors, 9 tasks, 5 datasets, 3 downstream architectures, 2 levels of magnification, and various preprocessing setups. Our findings challenge existing assumptions: 1) We observe empirically, and by analysing the latent space, that skipping stain normalisation and image augmentations does not degrade performance, while significantly reducing memory and computational demands. 2) We develop a novel evaluation metric to compare relative downstream performance, and show that the choice of feature extractor is the most consequential factor for downstream performance. 3) We find that lower-magnification slides are sufficient for accurate slide-level classification. Contrary to previous patch-level benchmarking studies, our approach emphasises clinical relevance by focusing on slide-level biomarker prediction tasks in a weakly supervised setting with external validation cohorts. Our findings stand to streamline digital pathology workflows by minimising preprocessing needs and informing the selection of feature extractors.
Non-Contact NIR PPG Sensing through Large Sequence Signal Regression
Authors: Timothy Hanley, Dara Golden, Robyn Maxwell, Ashkan Parsi, Joseph Lemley
Date: 2023-11-20 00:00:00
Tasks: Photoplethysmography (PPG), regression

Non-Contact sensing is an emerging technology with applications across many industries from driver monitoring in vehicles to patient monitoring in healthcare. Current state-of-the-art implementations focus on RGB video, but this struggles in varying/noisy light conditions and is almost completely unfeasible in the dark. Near Infra-Red (NIR) video, however, does not suffer from these constraints. This paper aims to demonstrate the effectiveness of an alternative Convolution Attention Network (CAN) architecture, to regress photoplethysmography (PPG) signal from a sequence of NIR frames. A combination of two publicly available datasets, which is split into train and test sets, is used for training the CAN. This combined dataset is augmented to reduce overfitting to the 'normal' 60 - 80 bpm heart rate range by providing the full range of heart rates along with corresponding videos for each subject. This CAN, when implemented over video cropped to the subject's head, achieved a Mean Average Error (MAE) of just 0.99 bpm, proving its effectiveness on NIR video and the architecture's feasibility to regress an accurate signal output.
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems
Authors: Sai Amrit Patnaik, Shivali Chansoriya, Anil K. Jain, Anoop M. Namboodiri
Date: 2023-11-20 00:00:00
Tasks: Adversarial Attack, Face Presentation Attack Detection, Generative Adversarial Network

Evaluating the risk level of adversarial images is essential for safely deploying face authentication models in the real world. Popular approaches for physical-world attacks, such as print or replay attacks, suffer from some limitations, like including physical and geometrical artifacts. Recently, adversarial attacks have gained attraction, which try to digitally deceive the learning strategy of a recognition system using slight modifications to the captured image. While most previous research assumes that the adversarial image could be digitally fed into the authentication systems, this is not always the case for systems deployed in the real world. This paper demonstrates the vulnerability of face authentication systems to adversarial images in physical world scenarios. We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting. Using this attack strategy, the attack success rate goes up to 82.01%. We test AdvGen extensively on four datasets and ten state-of-the-art PADs. We also demonstrate the effectiveness of our attack by conducting experiments in a realistic, physical environment.
Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Images
Authors: Mario Pascual González
Date: 2023-11-20 00:00:00
Tasks: Lesion Segmentation, Segmentation

In the realm of medical imaging, precise segmentation of stroke lesions from brain MRI images stands as a critical challenge with significant implications for patient diagnosis and treatment. Addressing this, our study introduces an innovative approach using a Fuzzy Information Seeded Region Growing (FISRG) algorithm. Designed to effectively delineate the complex and irregular boundaries of stroke lesions, the FISRG algorithm combines fuzzy logic with Seeded Region Growing (SRG) techniques, aiming to enhance segmentation accuracy. The research involved three experiments to optimize the FISRG algorithm's performance, each focusing on different parameters to improve the accuracy of stroke lesion segmentation. The highest Dice score achieved in these experiments was 94.2\%, indicating a high degree of similarity between the algorithm's output and the expert-validated ground truth. Notably, the best average Dice score, amounting to 88.1\%, was recorded in the third experiment, highlighting the efficacy of the algorithm in consistently segmenting stroke lesions across various slices. Our findings reveal the FISRG algorithm's strengths in handling the heterogeneity of stroke lesions. However, challenges remain in areas of abrupt lesion topology changes and in distinguishing lesions from similar intensity brain regions. The results underscore the potential of the FISRG algorithm in contributing significantly to advancements in medical imaging analysis for stroke diagnosis and treatment.
On the Importance of Large Objects in CNN Based Object Detection Algorithms
Authors: Ahmed Ben Saad, Gabriele Facciolo, Axel Davy
Date: 2023-11-20 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection models, a prominent class of machine learning algorithms, aim to identify and precisely locate objects in images or videos. However, this task might yield uneven performances sometimes caused by the objects sizes and the quality of the images and labels used for training. In this paper, we highlight the importance of large objects in learning features that are critical for all sizes. Given these findings, we propose to introduce a weighting term into the training loss. This term is a function of the object area size. We show that giving more weight to large objects leads to improved detection scores across all object sizes and so an overall improvement in Object Detectors performances (+2 p.p. of mAP on small objects, +2 p.p. on medium and +4 p.p. on large on COCO val 2017 with InternImage-T). Additional experiments and ablation studies with different models and on a different dataset further confirm the robustness of our findings.
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
Authors: Chi Yan, Delin Qu, Dan Xu, Bin Zhao, Zhigang Wang, Dong Wang, Xuelong Li
Date: 2023-11-20 00:00:00
Proceeding: CVPR 2024 1
Tasks: Pose Tracking, Simultaneous Localization and Mapping

In this paper, we introduce \textbf{GS-SLAM} that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance between efficiency and accuracy. Compared to recent SLAM methods employing neural implicit representations, our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Specifically, we propose an adaptive expansion strategy that adds new or deletes noisy 3D Gaussians in order to efficiently reconstruct new observed scene geometry and improve the mapping of previously observed areas. This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods. Moreover, in the pose tracking process, an effective coarse-to-fine technique is designed to select reliable 3D Gaussian representations to optimize camera pose, resulting in runtime reduction and robust estimation. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets. Project page: https://gs-slam.github.io/.
Cut-and-Paste: Subject-Driven Video Editing with Attention Control
Authors: Zhichao Zuo, Zhao Zhang, Yan Luo, Yang Zhao, Haijun Zhang, Yi Yang, Meng Wang
Date: 2023-11-20 00:00:00
Tasks: Object, Video Editing

This paper presents a novel framework termed Cut-and-Paste for real-word semantic video editing under the guidance of text prompt and additional reference image. While the text-driven video editing has demonstrated remarkable ability to generate highly diverse videos following given text prompts, the fine-grained semantic edits are hard to control by plain textual prompt only in terms of object details and edited region, and cumbersome long text descriptions are usually needed for the task. We therefore investigate subject-driven video editing for more precise control of both edited regions and background preservation, and fine-grained semantic generation. We achieve this goal by introducing an reference image as supplementary input to the text-driven video editing, which avoids racking your brain to come up with a cumbersome text prompt describing the detailed appearance of the object. To limit the editing area, we refer to a method of cross attention control in image editing and successfully extend it to video editing by fusing the attention map of adjacent frames, which strikes a balance between maintaining video background and spatio-temporal consistency. Compared with current methods, the whole process of our method is like ``cut" the source object to be edited and then ``paste" the target object provided by reference image. We demonstrate that our method performs favorably over prior arts for video editing under the guidance of text prompt and extra reference image, as measured by both quantitative and subjective evaluations.
Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement
Authors: Yanyan Wei, Zhao Zhang, Jiahuan Ren, Xiaogang Xu, Richang Hong, Yi Yang, Shuicheng Yan, Meng Wang
Date: 2023-11-20 00:00:00
Tasks: Image Restoration, Language Modelling

The generalization capability of existing image restoration and enhancement (IRE) methods is constrained by the limited pre-trained datasets, making it difficult to handle agnostic inputs such as different degradation levels and scenarios beyond their design scopes. Moreover, they are not equipped with interactive mechanisms to consider user preferences or feedback, and their end-to-end settings cannot provide users with more choices. Faced with the above-mentioned IRE method's limited performance and insufficient interactivity, we try to solve it from the engineering and system framework levels. Specifically, we propose Clarity ChatGPT-a transformative system that combines the conversational intelligence of ChatGPT with multiple IRE methods. Clarity ChatGPT can automatically detect image degradation types and select appropriate IRE methods to restore images, or iteratively generate satisfactory results based on user feedback. Its innovative features include a CLIP-powered detector for accurate degradation classification, no-reference image quality evaluation for performance evaluation, region-specific processing for precise enhancements, and advanced fusion techniques for optimal restoration results. Clarity ChatGPT marks a significant advancement in integrating language and vision, enhancing image-text interactions, and providing a robust, high-performance IRE solution. Our case studies demonstrate that Clarity ChatGPT effectively improves the generalization and interaction capabilities in the IRE, and also fills the gap in the low-level domain of the existing vision-language model.
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
Authors: Qingjie Zeng, Yutong Xie, Zilin Lu, Mengkang Lu, Yicheng Wu, Yong Xia
Date: 2023-11-20 00:00:00
Tasks: Benchmarking, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

Annotation scarcity has become a major obstacle for training powerful deep-learning models for medical image segmentation, restricting their deployment in clinical scenarios. To address it, semi-supervised learning by exploiting abundant unlabeled data is highly desirable to boost the model training. However, most existing works still focus on limited medical tasks and underestimate the potential of learning across diverse tasks and multiple datasets. Therefore, in this paper, we introduce a \textbf{Ver}satile \textbf{Semi}-supervised framework (VerSemi) to point out a new perspective that integrates various tasks into a unified model with a broad label space, to exploit more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, to capture cross-dataset semantics. Particularly, we create a synthetic task with a cutmix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint. This involves aligning aggregated predictions from various tasks with those from the synthetic task, further guiding the model in accurately segmenting foreground regions during training. We evaluated our VerSemi model on four public benchmarking datasets. Extensive experiments demonstrated that VerSemi can consistently outperform the second-best method by a large margin (e.g., an average 2.69\% Dice gain on four datasets), setting new SOTA performance for semi-supervised medical image segmentation. The code will be released.
PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification
Authors: Zhihan Yang, Zhiming Cheng, Tengjin Weng, Shucheng He, Yaqi Wang, Xin Ye, Shuai Wang
Date: 2023-11-20 00:00:00
Tasks: Multi-class Classification

Retinal disease is one of the primary causes of visual impairment, and early diagnosis is essential for preventing further deterioration. Nowadays, many works have explored Transformers for diagnosing diseases due to their strong visual representation capabilities. However, retinal diseases exhibit milder forms and often present with overlapping signs, which pose great difficulties for accurate multi-class classification. Therefore, we propose a new framework named Multi-Scale Patch Message Passing Swin Transformer for multi-class retinal disease classification. Specifically, we design a Patch Message Passing (PMP) module based on the Message Passing mechanism to establish global interaction for pathological semantic features and to exploit the subtle differences further between different diseases. Moreover, considering the various scale of pathological features we integrate multiple PMP modules for different patch sizes. For evaluation, we have constructed a new dataset, named OPTOS dataset, consisting of 1,033 high-resolution fundus images photographed by Optos camera and conducted comprehensive experiments to validate the efficacy of our proposed method. And the results on both the public dataset and our dataset demonstrate that our method achieves remarkable performance compared to state-of-the-art methods.
OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning
Authors: Haiyang Ying, Yixuan Yin, Jinzhi Zhang, Fan Wang, Tao Yu, Ruqi Huang, Lu Fang
Date: 2023-11-20 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Novel View Synthesis, Segmentation

Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework, which is accomplished by two steps. Firstly, we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly, image features rendered from the 3D feature field are clustered at different levels, which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations, this framework yields a global consistent 3D feature field, which further enables hierarchical segmentation, multi-object selection, and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation.
Enhanced Spatio-Temporal Context for Temporally Consistent Robust 3D Human Motion Recovery from Monocular Videos
Authors: Sushovan Chanda, Amogh Tiwari, Lokender Tiwari, Brojeshwar Bhowmick, Avinash Sharma, Hrishav Barua
Date: 2023-11-20 00:00:00
Tasks: Motion Estimation

Recovering temporally consistent 3D human body pose, shape and motion from a monocular video is a challenging task due to (self-)occlusions, poor lighting conditions, complex articulated body poses, depth ambiguity, and limited availability of annotated data. Further, doing a simple perframe estimation is insufficient as it leads to jittery and implausible results. In this paper, we propose a novel method for temporally consistent motion estimation from a monocular video. Instead of using generic ResNet-like features, our method uses a body-aware feature representation and an independent per-frame pose and camera initialization over a temporal window followed by a novel spatio-temporal feature aggregation by using a combination of self-similarity and self-attention over the body-aware features and the perframe initialization. Together, they yield enhanced spatiotemporal context for every frame by considering remaining past and future frames. These features are used to predict the pose and shape parameters of the human body model, which are further refined using an LSTM. Experimental results on the publicly available benchmark data show that our method attains significantly lower acceleration error and outperforms the existing state-of-the-art methods over all key quantitative evaluation metrics, including complex scenarios like partial occlusion, complex poses and even relatively low illumination.
Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images
Authors: Chi-en Amy Tai, Elizabeth Janes, Chris Czarnecki, Alexander Wong
Date: 2023-11-20 00:00:00

Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge; however these solutions leverage ensembles of complex deep neural architectures requiring immense storage and compute costs, and therefore may not be tractable. A recent movement for TinyML applications is integrating Double-Condensing Attention Condensers (DC-AC) into a self-attention neural network backbone architecture to allow for faster and more efficient computation. This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images. The final model is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer. Future work of this research includes iterating on the design of the selected network architecture and refining the approach to generalize to other forms of cancer.
Cancer-Net PCa-Data: An Open-Source Benchmark Dataset for Prostate Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data
Authors: Hayden Gunraj, Chi-en Amy Tai, Alexander Wong
Date: 2023-11-20 00:00:00
Tasks: Diversity

The recent introduction of synthetic correlated diffusion (CDI$^s$) imaging has demonstrated significant potential in the realm of clinical decision support for prostate cancer (PCa). CDI$^s$ is a new form of magnetic resonance imaging (MRI) designed to characterize tissue characteristics through the joint correlation of diffusion signal attenuation across different Brownian motion sensitivities. Despite the performance improvement, the CDI$^s$ data for PCa has not been previously made publicly available. In our commitment to advance research efforts for PCa, we introduce Cancer-Net PCa-Data, an open-source benchmark dataset of volumetric CDI$^s$ imaging data of PCa patients. Cancer-Net PCa-Data consists of CDI$^s$ volumetric images from a patient cohort of 200 patient cases, along with full annotations (gland masks, tumor masks, and PCa diagnosis for each tumor). We also analyze the demographic and label region diversity of Cancer-Net PCa-Data for potential biases. Cancer-Net PCa-Data is the first-ever public dataset of CDI$^s$ imaging data for PCa, and is a part of the global open-source initiative dedicated to advancement in machine learning and imaging research to aid clinicians in the global fight against cancer.