Towards Trainable Saliency Maps in Medical Imaging

• URL: http://arxiv.org/abs/2011.07482v1
• Date: Sun, 15 Nov 2020 09:01:55 GMT
• Title: Towards Trainable Saliency Maps in Medical Imaging
• Authors: Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, Praveer Singh
• Abstract summary: We show how introducing a model design element agnostic to both architecture complexity and model task gives an inherently self-explanatory model. We compare our results with state of the art non-trainable saliency maps on RSNA Pneumonia dataset and demonstrate a much higher localization efficacy using our adopted technique.
• Score: 4.438919530397659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread acceptability is severely limited by inherent black-box decision making and unsafe failure modes. While saliency methods attempt to tackle this problem in non-medical contexts, their apriori explanations do not transfer well to medical usecases. With this study we validate a model design element agnostic to both architecture complexity and model task, and show how introducing this element gives an inherently self-explanatory model. We compare our results with state of the art non-trainable saliency maps on RSNA Pneumonia Dataset and demonstrate a much higher localization efficacy using our adopted technique. We also compare, with a fully supervised baseline and provide a reasonable alternative to it's high data labelling overhead. We further investigate the validity of our claims through qualitative evaluation from an expert reader.

Related papers

• Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions [66.40971096248946]
  In this paper, we collect a series of MedISeg tricks for different model implementation phases. We experimentally explore the effectiveness of these tricks on consistent baselines. We also open-sourced a strong MedISeg repository, where each component has the advantage of plug-and-play.
  arXiv  Detail & Related papers  (2022-09-21T12:30:05Z)
• Explainable Deep Learning Methods in Medical Image Classification: A Survey [0.0]
  State-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data. These models are hardly adopted in clinical, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models.
  arXiv  Detail & Related papers  (2022-05-10T09:28:14Z)
• AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation [30.075714642990768]
  We present a novel deep learning framework that uses knowledge distillation through self-supervised learning and self-training. Experimental results show that the proposed framework maintains impressive robustness against a real-world environment. The proposed framework has a great potential for medical imaging, where plenty of data is accumulated every year.
  arXiv  Detail & Related papers  (2022-02-13T22:40:46Z)
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [58.26619456972598]
  Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL) We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme.
  arXiv  Detail & Related papers  (2021-06-16T07:58:00Z)
• An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification [3.7220380160016626]
  We propose a hybrid model for uncertainty quantification and an active learning approach for annotating the unlabelled data. We evaluate the proposed framework for diabetic retinopathy classification problem and have achieved state-of-the-art performance in terms of different metrics.
  arXiv  Detail & Related papers  (2020-12-24T16:59:15Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis [102.40869566439514]
  We seek to exploit rich labeled data from relevant domains to help the learning in the target task via Unsupervised Domain Adaptation (UDA) Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
  arXiv  Detail & Related papers  (2020-07-05T11:49:17Z)
• Assessing the validity of saliency maps for abnormality localization in medical imaging [6.299152745637685]
  Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network. In this work, we explored the credibility of the various existing saliency map methods on the RSNA Pneumonia dataset.
  arXiv  Detail & Related papers  (2020-05-29T20:17:26Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis [2.512212190779389]
  We present a model saliency mapping framework tailored to medical imaging. We replace techniques with a strong neighborhood conditioned inpainting approach, which avoids implausible artefacts.
  arXiv  Detail & Related papers  (2020-04-03T14:59:08Z)

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.