Medical image segmentation has played an important role in medical analysis and widely developed for many clinical applications. Deep learning-based approaches have achieved high performance in semantic segmentation but they are limited to pixel-wise setting and imbalanced classes data problem. In this paper, we tackle those limitations by developing a new deep learning-based model which takes into account both higher feature level i.e. region inside contour, intermediate feature level i.e. offset curves around the contour and lower feature level i.e. contour. Our proposed Offset Curves (OsC) loss consists of three main fitting terms. The first fitting term focuses on pixel-wise level segmentation whereas the second fitting term acts as attention model which pays attention to the area around the boundaries (offset curves). The third terms plays a role as regularization term which takes the length of boundaries into account. We evaluate our proposed OsC loss on both 2D network and 3D network. Two common medical datasets, i.e. retina DRIVE and brain tumor BRATS 2018 datasets are used to benchmark our proposed loss performance. The experiments have shown that our proposed OsC loss function outperforms other mainstream loss functions such as Cross-Entropy, Dice, Focal on the most common segmentation networks Unet, FCN.
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation
Beijing Institute of Technology（北京理工大学）；School of Computer Science（计算机学院）；Beijing Lab of Intelligent Information Technology（北京智能信息技术实验室）；Chinese Academy of Medical Sciences（北京协和医学院（中国医学科学院））
Boston Children's Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased towards the non-lesion class which is particularly undesired in most medical applications where…
Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-…
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）
University of Pittsburgh（匹兹堡大学）；National University of Defense Technology（国防科技大学）；University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh（匹兹堡大学医学中心匹兹堡儿童医院）；Magee-Womens Hospital of University of Pittsburgh Medical Center（匹兹堡大学医学中心马吉妇女医院）
Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-base…
DMAF-Net: An Effective Modality Rebalancing Framework for Incomplete Multi-Modal Medical Image Segmentation
College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, \textit{domain shift} and \textit{corrupted annotations}, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice. In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selec…
Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation
Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Informatics Institute, Faculty of Science（信息学院，科学学院）；Department of Radiology（放射科）；Mayo Clinic Rochester（罗切斯特梅奥诊所）
University of Cambridge（剑桥大学）；University of Salerno（萨莱诺大学）
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background. Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence. The most commonly used loss functions for segmentati…
We propose a highly data-efficient active learning framework for image classification. Our novel framework combines: (1) unsupervised representation learning of a Convolutional Neural Network and (2) the Gaussian Process (GP) method, in sequence to achieve highly data and label efficient classifications. Moreover, both elements are less sensitive to the prevalent and challenging class imbalance issue, thanks to the (1) feature learned without labels and (2) the Bayesian nature of GP. The GP-provided uncertainty estimates enable active learning by ranking samples based on the uncertainty and se…
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；Stanford University（斯坦福大学）
In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge. Under these circumstances, the predicted class probabilities may be biased toward the majority class. Calibration has been suggested to alleviate some of these effects. However, there is insufficient analysis explaining when and whether calibrating a model would be beneficial in improving performance. In this study, we perform a systematic analys…
University of Pittsburgh（匹兹堡大学）；National University of Defense Technology（国防科技大学）；University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh（匹兹堡大学医学中心匹兹堡儿童医院）
Medical image data are usually imbalanced across different classes. One-class classification has attracted increasing attention to address the data imbalance problem by distinguishing the samples of the minority class from the majority class. Previous methods generally aim to either learn a new feature space to map training samples together or to fit training samples by autoencoder-like models. These methods mainly focus on capturing either compact or descriptive features, where the information of the samples of a given one class is not sufficiently utilized. In this paper, we propose a novel …
Edith Cowan University（伊迪丝·考恩大学）；University of Western Australia（西澳大学）
Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfe…
Technical University of Munich (TUM)（慕尼黑工业大学）；Kyung Hee University（庆熙大学）；University of Munich (LMU)（慕尼黑大学）
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced. Training a model on an imbalanced dataset can introduce unique challenges to the learning problem where a model is biased towards the highly frequent class. Many methods are proposed to tackle the distributional differences and the imbalanced problem. However, the impact of these approaches on the learned features is not well studied. In this paper, we look deeper into the internal units of neural networks to observe how han…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；AMEOS Hospital Bernburg（AMEOS 伯恩堡医院）
Background and objective: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. On selected benchmarking datasets, it outperformed state-of-the-art methods within classification and segmentation tasks. Especially, the medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances of MeshCNN on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion for comple…
School of Professional Studies（专业研究学院）；Northwestern University（西北大学）
Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to ind…
CALF: A Conditionally Adaptive Loss Function to Mitigate Class-Imbalanced Segmentation
Åbo Akademi University（奥博学术大学）；School of Engineering, University of Melbourne（墨尔本大学工程学院）；Delft University of Technology（代尔夫特理工大学）；Sorbonne Université（索邦大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye & Ear Hospital（维多利亚皇家眼耳医院）
Universidad Politécnica de Madrid（马德里理工大学）；Universidad Autónoma de Madrid（马德里自治大学）；TECNALIA（特克纳利亚研究中心）；Basque Research & Technology Alliance（巴斯克研究与技术联盟）
Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary …
