Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
The Chinese University of Hong Kong（香港中文大学）；Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院广东省计算机视觉与虚拟现实技术重点实验室）
Deep learning methods show promising results for overlapping cervical cell instance segmentation. However, in order to train a model with good generalization ability, voluminous pixel-level annotations are demanded which is quite expensive and time-consuming for acquisition. In this paper, we propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining (MMT-PSM), which consists of a teacher and a student network during training. Two networks are encouraged to be consistent both in feature and semantic level under small perturbations. The teacher's self-ensemble predictions from $K$-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student. We design a novel strategy to estimate the sensitivity to perturbations for each proposal and select informative samples from massive cases to facilitate fast and effective semantic distillation. In addition, to eliminate the unavoidable noise from the background region, we propose to use the predicted segmentation mask as guidance to enforce the feature distillation in the foreground region. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only, and outperforms state-of-the-art semi-supervised methods.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人学国家重点实验室）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Manufacturing Industrial Integrated, Shenyang University（沈阳大学制造工业集成重点实验室）；Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute（中国医科大学附属肿瘤医院（辽宁省肿瘤医院暨研究所））
Accurate labeling is essential for supervised deep learning methods. However, it is almost impossible to accurately and manually annotate thousands of images, which results in many labeling errors for most datasets. We proposes a local label point correction (LLPC) method to improve annotation quality for edge detection and image segmentation tasks. Our algorithm contains three steps: gradient-guided point correction, point interpolation and local point smoothing. We correct the labels of object contours by moving the annotated points to the pixel gradient peaks. This can improve the edge loca…
RWTH Aachen University（亚琛工业大学）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
Instance segmentation aims to delineate each individual object of interest in an image. State-of-the-art approaches achieve this goal by either partitioning semantic segmentations or refining coarse representations of detected objects. In this work, we propose a novel approach to solve the problem via object layering, i.e. by distributing crowded, even overlapping objects into different layers. By grouping spatially separated objects in the same layer, instances can be effortlessly isolated by extracting connected components in each layer. In comparison to previous methods, our approach is not…
Indian Institute of Technology Kharagpur（印度理工学院卡拉普尔）
Overlapping of cervical cells and poor contrast of cell cytoplasm are the major issues in accurate detection and segmentation of cervical cells. An unsupervised cell segmentation approach is presented here. Cell clump segmentation was carried out using the extended depth of field (EDF) image created from the images of different focal planes. A modified Otsu method with prior class weights is proposed for accurate segmentation of nuclei from the cell clumps. The cell cytoplasm was further segmented from cell clump depending upon the number of nucleus detected in that cell clump. Level set model…
National Institute of Technology, Tiruchirappalli（国家理工学院蒂鲁吉拉伯利分校）；Indian Institute of Technology, Kharagpur（印度理工学院克勒格布尔分校）
The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accurate segmentation of cervical cell cytoplasm. This paper presents an automated unsupervised cytoplasm segmentation approach which can effectively find the cytoplasm boundaries in overlapping cells. The proposed approach first segments the cell clumps from the cervical smear image and detects the nuclei in each cell clump. A modified Otsu method with prior class probability is proposed for accurate segmentation of nuclei from the cell clumps. Using distance regularized level set evolution, the conto…
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Co., Ltd.（视见医疗科技有限公司）；Imperial College London（帝国理工学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Cell instance segmentation in Pap smear image remains challenging due to the wide existence of occlusion among translucent cytoplasm in cell clumps. Conventional methods heavily rely on accurate nuclei detection results and are easily disturbed by miscellaneous objects. In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction. Specifically, we propose the Instance Relation Module to construct the cell association matrix for transferring information among individual cell-instance features. With the co…
Two-Stage Cross-Domain Cervical Abnormality Screening with Cytopathological Image Synthesis and Knowledge Distillation
School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）；School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）
University of Southern Maine（南缅因大学）；University of South Florida（南佛罗里达大学）；SRC Biosciences（SRC生物科学公司）
Analyzing Pap cytology slides is an important tasks in detecting and grading precancerous and cancerous cervical cancer stages. Processing cytology images usually involve segmenting nuclei and overlapping cells. We introduce a cervical cytology dataset that can be used to evaluate nucleus detection, as well as image classification methods in the cytology image processing area. This dataset contains 93 real image stacks with their grade labels and manually annotated nuclei within images. We also present two methods: a baseline method based on a previously proposed approach, and a deep learning …
The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）；Shanghai Jiao Tong University（上海交通大学）
Segmenting overlapping cytoplasm of cells in cervical smear images is a clinically essential task, for quantitatively measuring cell-level features in order to diagnose cervical cancer. This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region. Although shape prior-based models that compensate intensity deficiency by introducing prior shape information (shape priors) about cytoplasm are firmly established, they often yield visually implausible results, mainly because they model shape priors only by limited shape h…
MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection
Southwest Minzu University（西南民族大学）；The Australian National University（澳大利亚国立大学）；West China Hospital（华西医院）；Sichuan University（四川大学）；La Trobe University（拉筹伯大学）
Missouri University of Science and Technology（密苏里科技大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔生物医学通信中心）；U.S. National Library of Medicine（美国国家医学图书馆）；Diagnostic Systems Women’s Health and Cancer（诊断系统女性健康与癌症部门）；Becton Dickinson and Company（碧迪公司）
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Most methods published in the research literature rely on accurate cell segmentation as a prior, which remains challenging due to a variety of factors, e.g., stain consistency, presence of clustered cells, etc. We propose a method for automatic classification of cervical slide images through generation of labeled cervical patch data and extracting dee…
Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery
National University of Singapore（新加坡国立大学）；NUSRI Suzhou（新加坡国立大学苏州研究院）；University College London（伦敦大学学院）；The Chinese University of Hong Kong（香港中文大学）；Shun Hing Institute of Advanced Engineering（信兴高等工程研究院）
HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation
University of Information Technology（信息科技大学）；Ho Chi Minh University of Technology（胡志明市技术大学）；University of Science（科学大学）；Vietnam National University（越南国家大学）；Boston University（波士顿大学）；Taipei Medical University（台北医学大学）；Posts and Telecommunications Institute of Technology（邮电技术学院）；New Mexico State University（新墨西哥州立大学）
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
UT Austin（德克萨斯大学奥斯汀分校）；Weill Cornell Medicine（威尔康奈尔医学院）
AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-quality datasets due to high annotation cost, inter-observer variability, human annotator error, and errors in computer-generated labels. Deep learning models trained on noisy labelled datasets are sensitive to the noise type and lead to less generalization on the unseen samples. To address this challenge, we propose a Robust Stochastic Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a to…
Ecole de technologie superieure（高等技术学院）；Aligarh Muslim University（阿里格尔穆斯林大学）；Genetec Inc.（Genetec公司）
Beyond the complexity of CNNs that require training on large annotated datasets, the domain shift between design and operational data has limited the adoption of CNNs in many real-world applications. For instance, in person re-identification, videos are captured over a distributed set of cameras with non-overlapping viewpoints. The shift between the source (e.g. lab setting) and target (e.g. cameras) domains may lead to a significant decline in recognition accuracy. Additionally, state-of-the-art CNNs may not be suitable for such real-time applications given their computational requirements. A…
Relational Knowledge Distillation Brings DNN Representations Close Enough to Humans to Be Aligned Without Supervision
Graduate School of Arts and Sciences, The University of Tokyo（东京大学文学部与科学研究科）；Graduate School of Engineering Science, The University of Osaka（大阪大学工程科学研究科）
