Overcoming Uncertain Incompleteness for Robust Multimodal Sequential Diagnosis Prediction via Knowledge Distillation and Random Data Erasing

• URL: http://arxiv.org/abs/2407.19540v2
• Date: Tue, 10 Sep 2024 20:43:47 GMT
• Title: Overcoming Uncertain Incompleteness for Robust Multimodal Sequential Diagnosis Prediction via Knowledge Distillation and Random Data Erasing
• Authors: Heejoon Koo,
• Abstract summary: We modify NECHO, designed in a diagnosis code-centric fashion, to handle uncertain modality representation dominance under imperfect data. We develop a systematic knowledge distillation by employing the modified NECHO as both teacher and student. We also utilise random erasing on individual data points within sequences during both training and distillation of the teacher to lightly simulate scenario with missing visit information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we present NECHO v2, a novel framework designed to enhance the predictive accuracy of multimodal sequential patient diagnoses under uncertain missing visit sequences, a common challenge in real clinical settings. Firstly, we modify NECHO, designed in a diagnosis code-centric fashion, to handle uncertain modality representation dominance under the imperfect data. Secondly, we develop a systematic knowledge distillation by employing the modified NECHO as both teacher and student. It encompasses a modality-wise contrastive and hierarchical distillation, transformer representation random distillation, along with other distillations to align representations between teacher and student tightly and effectively. We also utilise random erasing on individual data points within sequences during both training and distillation of the teacher to lightly simulate scenario with missing visit information, thereby fostering effective knowledge transfer. As a result, NECHO v2 verifies itself by showing robust superiority in multimodal sequential diagnosis prediction under both balanced and imbalanced incomplete settings on multimodal healthcare data.

Related papers

• Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography [6.540741143328299]
  The acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details. We propose Re-Training for Uncertainty (RT4U), a data-centric method to introduce uncertainty to weakly informative inputs in the training set. When combined with conformal prediction techniques, RT4U can yield adaptively sized prediction sets which are guaranteed to contain the ground truth class to a high accuracy.
  arXiv  Detail & Related papers  (2024-09-15T10:06:06Z)
• Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation [10.65123164779962]
  Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers. We propose a novel Human-in-the-loop TTA framework that capitalizes on the largely overlooked potential of clinician-corrected predictions. Our framework conceives a divergence loss, designed specifically to diminish the prediction divergence instigated by domain disparities.
  arXiv  Detail & Related papers  (2024-05-14T02:02:15Z)
• Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection [19.099643719358692]
  We propose a simple yet effective two-stage industrial anomaly detection framework, termed as AAND. In the first anomaly amplification stage, we propose a novel Residual Anomaly Amplification (RAA) module to advance the pre-trained teacher encoder. We further employ a reverse distillation paradigm to train a student decoder, in which a novel Hard Knowledge Distillation (HKD) loss is built to better facilitate the reconstruction of normal patterns.
  arXiv  Detail & Related papers  (2024-05-03T13:00:22Z)
• 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)
• Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions [80.88681952022479]
  We introduce a new method for combining semi-supervised with multiple-instance learning. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains in per-subject tremor detection.
  arXiv  Detail & Related papers  (2023-04-29T12:25:10Z)
• Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases [66.27889778566734]
  Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge. An adapter-based Continual Learning framework called ACL is proposed to help effectively learn a set of new diseases.
  arXiv  Detail & Related papers  (2023-04-18T15:01:45Z)
• Multimodal Explainability via Latent Shift applied to COVID-19 stratification [0.7831774233149619]
  We present a deep architecture, which jointly learns modality reconstructions and sample classifications. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset.
  arXiv  Detail & Related papers  (2022-12-28T20:07:43Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [49.1861463923357]
  We propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins.
  arXiv  Detail & Related papers  (2021-04-07T16:23:35Z)
• 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.