LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning

• URL: http://arxiv.org/abs/2411.01144v1
• Date: Sat, 02 Nov 2024 05:27:52 GMT
• Title: LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning
• Authors: Gautam Gare, Jana Armouti, Nikhil Madaan, Rohan Panda, Tom Fox, Laura Hutchins, Amita Krishnan, Ricardo Rodriguez, Bennett DeBoisblanc, Deva Ramanan, John Galeotti,
• Abstract summary: Can a model trained on multi-patient scans predict subtle changes in an individual patient's scans? Recent computer vision models to learn fine-grained differences while being trained on data showing larger differences. We find that models pre-trained on clips from multiple patients can better predict fine-grained differences in scans from a single patient by employing contrastive learning.
• Score: 28.56726678583327
• License:
• Abstract: A crucial question in active patient care is determining if a treatment is having the desired effect, especially when changes are subtle over short periods. We propose using inter-patient data to train models that can learn to detect these fine-grained changes within a single patient. Specifically, can a model trained on multi-patient scans predict subtle changes in an individual patient's scans? Recent years have seen increasing use of deep learning (DL) in predicting diseases using biomedical imaging, such as predicting COVID-19 severity using lung ultrasound (LUS) data. While extensive literature exists on successful applications of DL systems when well-annotated large-scale datasets are available, it is quite difficult to collect a large corpus of personalized datasets for an individual. In this work, we investigate the ability of recent computer vision models to learn fine-grained differences while being trained on data showing larger differences. We evaluate on an in-house LUS dataset and a public ADNI brain MRI dataset. We find that models pre-trained on clips from multiple patients can better predict fine-grained differences in scans from a single patient by employing contrastive learning.

Related papers

• How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers? [49.35105290167996]
  Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-08-17T20:40:30Z)
• Time-based Self-supervised Learning for Wireless Capsule Endoscopy [1.3514953384460016]
  This work proposes using self-supervised learning for wireless endoscopy videos by introducing a custom-tailored method. We prove that using the inferred inherent structure learned by our method, extracted from the temporal axis, improves the detection rate on several domain-specific applications even under severe imbalance.
  arXiv  Detail & Related papers  (2022-04-20T20:31:06Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation [7.2666838978096875]
  Existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data. Five state-of-the-art seizure prediction methods are trained on the CHB-MIT sEEG database with our proposed scheme.
  arXiv  Detail & Related papers  (2022-02-25T10:30:29Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• Sequential Diagnosis Prediction with Transformer and Ontological Representation [35.88195694025553]
  We propose an end-to-end robust transformer-based model called SETOR to handle irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR achieves better predictive results than previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-09-07T13:09:55Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns [3.5939555573102857]
  We show how to extract features relevant to accelerometer gait data for Parkinson's disease classification. Our pre-trained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.
  arXiv  Detail & Related papers  (2020-05-06T04:08:19Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.