Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection

• URL: http://arxiv.org/abs/2004.04455v1
• Date: Thu, 9 Apr 2020 09:53:11 GMT
• Title: Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection
• Authors: Tiancheng Lin, Yuanfan Guo, Canqian Yang, Jiancheng Yang and Yi Xu
• Abstract summary: We propose Decoupled Gradient Harmonizing Mechanism (DGHM) and embed it into classification loss, denoted as DGHM-C loss. Without whistles and bells, we achieved the 2nd place in the challenge.
• Score: 13.530905176008057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection on signet ring cell (SRC) has not been thoroughly investigated. In MICCAI DigestPath2019 challenge, apart from foreground (SRC region)-background (normal tissue area) class imbalance, SRCs are partially annotated due to costly medical image annotation, which introduces extra label noise. To address the issues simultaneously, we propose Decoupled Gradient Harmonizing Mechanism (DGHM) and embed it into classification loss, denoted as DGHM-C loss. Specifically, besides positive (SRCs) and negative (normal tissues) examples, we further decouple noisy examples from clean examples and harmonize the corresponding gradient distributions in classification respectively. Without whistles and bells, we achieved the 2nd place in the challenge. Ablation studies and controlled label missing rate experiments demonstrate that DGHM-C loss can bring substantial improvement in partially annotated object detection.

Related papers

• SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising [15.472398279233515]
  Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. We propose a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising.
  arXiv  Detail & Related papers  (2024-02-06T08:48:39Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations [0.6733204971296001]
  We propose cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC.
  arXiv  Detail & Related papers  (2023-07-14T10:05:37Z)
• Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability [85.1044381834036]
  We investigate the implicit regularization effects of label noises under mini-batch sampling settings of gradient descent. We find such implicit regularizer would favor some convergence points that could stabilize model outputs against perturbation of parameters. Our work doesn't assume SGD as an Ornstein-Uhlenbeck like process and achieve a more general result with convergence of approximation proved.
  arXiv  Detail & Related papers  (2023-04-01T14:09:07Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals [7.182731690965173]
  We propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations.
  arXiv  Detail & Related papers  (2022-09-19T13:54:51Z)
• Sample hardness based gradient loss for long-tailed cervical cell detection [40.503143547742866]
  We propose aGrad-Libra Loss to dynamically calibrate the degree of hardness of each sample for different categories. Our loss can thus help the detector to put more emphasis on those hard samples in both head and tail categories.
  arXiv  Detail & Related papers  (2022-08-07T17:52:29Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images [83.7047542725469]
  Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need of external training data. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming the state-of-the-art alternatives, even while learning from a single slide.
  arXiv  Detail & Related papers  (2021-09-22T15:06:06Z)
• Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation [72.0276067144762]
  We discuss and define the two common types of label noise in medical images. We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
  arXiv  Detail & Related papers  (2021-02-28T14:56:45Z)
• Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images [20.480034690570196]
  We propose a straightforward approach to enforce the constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images. We expand standard classification losses with an extra term that acts as a regularizer. We show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading.
  arXiv  Detail & Related papers  (2020-10-01T10:42:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.