Localizing thoracic diseases on chest X-ray plays a critical role in clinical practices such as diagnosis and treatment planning. However, current deep learning based approaches often require strong supervision, e.g. annotated bounding boxes, for training such systems, which is infeasible to harvest in large-scale. We present Probabilistic Class Activation Map (PCAM) pooling, a novel global pooling operation for lesion localization with only image-level supervision. PCAM pooling explicitly leverages the excellent localization ability of CAM during training in a probabilistic fashion. Experiments on the ChestX-ray14 dataset show a ResNet-34 model trained with PCAM pooling outperforms state-of-the-art baselines on both the classification task and the localization task. Visual examination on the probability maps generated by PCAM pooling shows clear and sharp boundaries around lesion regions compared to the localization heatmaps generated by CAM. PCAM pooling is open sourced at https://github.com/jfhealthcare/Chexpert.
Isfahan University of Technology（伊斯法罕理工大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Michigan（密歇根大学）；McMaster University（麦克马斯特大学）
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in skin images. This network leads to highly accurate segmentation of lesions on skin lesion datasets which outperforms state-of-the-art algorithms …
Universitat Rovira i Virgili（罗维拉-维尔吉利大学）；Bioinformatics Institute（生物信息学研究所）；Rovira i Virgili University（罗维拉-维尔吉利大学）；Liverpool School of Tropical Medicine（利物浦热带医学院）；University of Barcelona（巴塞罗那大学）
Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melano…
MIT（麻省理工学院）；Philips Research North America（飞利浦北美研究院）
We present a fully convolutional neural network for segmenting ischemic stroke lesions in CT perfusion images for the ISLES 2018 challenge. Treatment of stroke is time sensitive and current standards for lesion identification require manual segmentation, a time consuming and challenging process. Automatic segmentation methods present the possibility of accurately identifying lesions and improving treatment planning. Our model is based on the PSPNet, a network architecture that makes use of pyramid pooling to provide global and local contextual information. To learn the varying shapes of the le…
Are Explanations Helpful? A Comparative Analysis of Explainability Methods in Skin Lesion Classifiers
Instituto de Computação, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil（坎皮纳斯州立大学计算研究所（UNICAMP））；Department of Computer Science, Universidad Católica San Pablo, Arequipa, Peru（天主教圣巴布罗大学计算机科学系）；Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism UDEM, University of Bern, Switzerland（伯尔尼大学糖尿病、内分泌学、营养医学和代谢学系）
Weakly Supervised Semantic Segmentation (WSSS) research has explored many directions to improve the typical pipeline CNN plus class activation maps (CAM) plus refinements, given the image-class label as the only supervision. Though the gap with the fully supervised methods is reduced, further abating the spread seems unlikely within this framework. On the other hand, WSSS methods based on Vision Transformers (ViT) have not yet explored valid alternatives to CAM. ViT features have been shown to retain a scene layout, and object boundaries in self-supervised learning. To confirm these findings, …
Weakly-Supervised Semantic Segmentation (WSSS) methods with image-level labels generally train a classification network to generate the Class Activation Maps (CAMs) as the initial coarse segmentation labels. However, current WSSS methods still perform far from satisfactorily because their adopted CAMs 1) typically focus on partial discriminative object regions and 2) usually contain useless background regions. These two problems are attributed to the sole image-level supervision and aggregation of global information when training the classification networks. In this work, we propose the visual…
Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
Beijing University of Technology（北京工业大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Institut du Cerveau – Paris Brain Institute - ICM（巴黎脑研究所）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公立医院集团）
Automatic lesion segmentation on thoracic CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, obtaining a large amount of voxel-level annotations for training segmentation networks is prohibitively expensive. Therefore, we propose a weakly-supervised segmentation method based on dense regression activation maps (dRAMs). Most weakly-supervised segmentation approaches exploit class activation maps (CAMs) to localize objects. However, because CAMs were trained for classification, they do not align precisely with the object segmentations. Instead, we produce…
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tencent AI Lab（腾讯人工智能实验室）
Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weak…
McGill University（麦吉尔大学）；MILA (Quebec Artificial Intelligence Institute)（魁北克人工智能研究所（MILA））；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（阿兰·图灵研究所）；NeuroRx Research（NeuroRx研究公司）
Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts. One strategy to create a more diverse, generalizable training set is to naively pool datasets from different cohorts. Surprisingly, training on this \it{big data} does not necessarily increase, and may even reduce, overall performance and model generalizability, due to the existence of cohort biases that affect label distributions. In this paper,…
Arizona State University（亚利桑那州立大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Children’s Hospital Los Angeles（洛杉矶儿童医院）；Mayo Clinic（梅奥诊所）
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain only image-level labels rather than pixel-based annotations. This motivates us to develop algorithms to classify rDR and segment lesions via image-level labels. This paper leverages self-supervised equivariant learning and attention-based multi-instance learning (MIL) to tackle this problem. MIL is an effective strategy to differentiate positive and negative instances, helping us discard background regions (negative i…
Seoul National University（首尔国立大学）；Seoul National University Hospital（首尔国立大学医院）
A meningioma is a type of brain tumor that requires tumor volume size follow ups in order to reach appropriate clinical decisions. A fully automated tool for meningioma detection is necessary for reliable and consistent tumor surveillance. There have been various studies concerning automated lesion detection. Studies on the application of convolutional neural network (CNN)-based methods, which have achieved a state-of-the-art level of performance in various computer vision tasks, have been carried out. However, the applicable diseases are limited, owing to a lack of strongly annotated data bei…
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China（计算机科学与技术学院（数据科学学院），太原科技大学，太原，山西，030024，中国）；College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China（人工智能学院，太原科技大学，太原，山西，030024，中国）；School of mathematics, Taiyuan University of Technology, Taiyuan, 030024, China（数学学院，太原科技大学，太原，030024，中国）；School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China（人工智能学院，北京师范大学，北京，100875，中国）；National Institute for Data Science in Health（健康数据科学国家研究院）；the Department of Computer Science, School of Informatics, Xiammen, 361005, China（信息学院计算机科学系，厦门，361005，中国）；School of Software, Dalian University of Technology, Dalian,116621, China（软件学院，大连理工大学，大连，116621，中国）
In medical imaging, Class-Activation Map (CAM) serves as the main explainability tool by pointing to the region of interest. Since the localization accuracy from CAM is constrained by the resolution of the model's feature map, one may expect that segmentation models, which generally have large feature maps, would produce more accurate CAMs. However, we have found that this is not the case due to task mismatch. While segmentation models are developed for datasets with pixel-level annotation, only image-level annotation is available in most medical imaging datasets. Our experiments suggest that …
Shandong University（山东大学）；Institute of Tsinghua-Berkeley Shenzhen, Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院清华-伯克利深圳学院）；Hangzhou Dianzi University（杭州电子科技大学）；Shenzhen ZNV Technology Co., Ltd（深圳中兴网信科技有限公司）；Shenzhen Graduate School of Harbin Institute of Technology（哈尔滨工业大学深圳研究生院）；Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院生物医药与健康工程研究院）
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions. However, training these segmentation models requires a large number of manually annotated pixel-level labels, which are time-consuming and labor-intensive, in contrast to image-level labels that are easier to obtain. It is imperative to resolve this problem through weakly-supervised semantic segmentation models using image-…
Extracting class activation maps (CAM) is arguably the most standard step of generating pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the crux of the unsatisfactory pseudo masks is the binary cross-entropy loss (BCE) widely used in CAM. Specifically, due to the sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to multiple classes co-occurring in the same receptive field. As a result, given a class, its hot CAM pixels may wrongly invade the area belonging to other classes, or the non-hot ones may be actually a part of the class. To th…
Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Computer Science, Peking University（北京大学计算机科学学院）；School of Future Technology, Tianjin University（天津大学未来技术学院）；Research Institute, Sun Yat-sen University（中山大学研究学院）
Weakly supervised video object localization (WSVOL) allows locating object in videos using only global video tags such as object class. State-of-art methods rely on multiple independent stages, where initial spatio-temporal proposals are generated using visual and motion cues, then prominent objects are identified and refined. Localization is done by solving an optimization problem over one or more videos, and video tags are typically used for video clustering. This requires a model per-video or per-class making for costly inference. Moreover, localized regions are not necessary discriminant b…
École de technologie supérieure（高等技术学院）；Ericsson（爱立信）；McGill University（麦吉尔大学）
Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretation without training on fully annotated image datasets. CAM methods are typically integrated within off-the-shelf CNN backbones, such as ResNet50. Due to convolution and pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, contributing to inaccurate localizations. Interpolation is required to restore full size CAMs, yet it does not consider the statistical properties of…
TU Munich（慕尼黑工业大学）；Universitat Pompeu Fabra（庞培法布拉大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；Ludwig Maximilian University（路德维希-马克西米利安大学）；CNRS（法国国家科学研究中心）；Ecole Centrale Nantes（南特中央理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system. Although primarily dealing with an image classification problem, we argue that localizing the fracture in the image is crucial to make good class predictions. Therefore, we propose and thoroughly analyze several schemes for simultaneous fracture localization and classification. We show that using an auxiliary localization task, in general, improves the classification performance. Moreover, it is possible to avoid the need for additional localiza…
ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images
TheraPanacea；University of Crete（克里特大学）；IACM, FORTH（FORTH 应用与计算数学研究所）；Archimedes, Athena RC（雅典研究中心阿基米德研究所）；CentraleSupélec, University of Paris-Saclay（中央理工-高等电力学院，巴黎-萨克雷大学）
