Conditional Training with Bounding Map for Universal Lesion Detection

• URL: http://arxiv.org/abs/2103.12277v1
• Date: Tue, 23 Mar 2021 03:04:13 GMT
• Title: Conditional Training with Bounding Map for Universal Lesion Detection
• Authors: Han Li, Long Chen, Hu Han, S. Kevin Zhou
• Abstract summary: Universal Lesion Detection in computed tomography plays an essential role in computer-aided diagnosis. Two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object proposal. We propose a BM-based conditional training for two-stage ULD, which can reduce positive vs. negative anchor imbalance.
• Score: 33.24904644311758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by coarse-to-fine two-stage detection approaches, but such two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object proposal and insufficient supervision problem during localization regression and classification of the region of interest (RoI) proposals. While leveraging pseudo segmentation masks such as bounding map (BM) can reduce the above issues to some degree, it is still an open problem to effectively handle the diverse lesion shapes and sizes in ULD. In this paper, we propose a BM-based conditional training for two-stage ULD, which can (i) reduce positive vs. negative anchor imbalance via BM-based conditioning (BMC) mechanism for anchor sampling instead of traditional IoU-based rule; and (ii) adaptively compute size-adaptive BM (ABM) from lesion bounding box, which is used for improving lesion localization accuracy via ABMsupervised segmentation. Experiments with four state-of-the-art methods show that the proposed approach can bring an almost free detection accuracy improvement without requiring expensive lesion mask annotations.

Related papers

• Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models [7.197545510697077]
  Bone marrow lesions (BMLs) are critical indicators of knee osteoarthritis (OA) effective detection of BMLs in MRI is vital for OA diagnosis and treatment. This paper proposes a semi-supervised local anomaly detection method using mask inpainting models for identification of BMLs in high-resolution knee MRI.
  arXiv  Detail & Related papers  (2024-09-27T23:47:47Z)
• Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations [49.33388736227072]
  We propose a semi- and weakly-supervised learning framework for mass segmentation. We use limited strongly-labeled samples and sufficient weakly-labeled samples to achieve satisfactory performance. Experiments on CBIS-DDSM and INbreast datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-03-14T12:05:25Z)
• Semi-weakly-supervised neural network training for medical image registration [18.520388065729552]
  This paper describes a semi-weakly-supervised registration pipeline that improves the model performance. We examine two types of augmentation methods by perturbation on network weights and image resampling. Experiments using 589 male pelvic MR images, labelled with eight anatomical ROIs, show the improvement in registration performance.
  arXiv  Detail & Related papers  (2024-02-16T14:44:40Z)
• Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detection [24.145060992747077]
  We propose a Likelihood-Aware Semantic Alignment (LSA) framework to promote the image-text correspondence into semantically high-likelihood regions. Extensive experiments demonstrate the remarkable OOD detection performance of our proposed LSA, surpassing existing methods by a margin of $15.26%$ and $18.88%$ on two F-OOD benchmarks.
  arXiv  Detail & Related papers  (2023-12-04T08:53:59Z)
• Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation [3.1671604920729224]
  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 paper leverages self-supervised equivariant learning and attention-based multi-instance learning to tackle this problem. We conduct extensive validation experiments on the Eyepacs dataset, achieving an area under the receiver operating characteristic curve (AU ROC) of 0.958, outperforming current state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-10-12T06:26:05Z)
• 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)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation [11.910765921234333]
  We propose a novel two-stage deep learning (DL) framework to improve the localization robustness. The first stage identifies and crops the thalamus regions from the whole brain MRI. The second stage performs per-voxel regression on the cropped volume to localize the targets at the finest resolution scale.
  arXiv  Detail & Related papers  (2020-11-03T23:43:52Z)
• Performance of Dual-Augmented Lagrangian Method and Common Spatial Patterns applied in classification of Motor-Imagery BCI [68.8204255655161]
  Motor-imagery based brain-computer interfaces (MI-BCI) have the potential to become ground-breaking technologies for neurorehabilitation. Due to the noisy nature of the used EEG signal, reliable BCI systems require specialized procedures for features optimization and extraction.
  arXiv  Detail & Related papers  (2020-10-13T20:50:13Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.