AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data

by   Zeyuan Wang, et al.

In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task. In this work, we introduce a novel multi-instance neural network, AMI-Net+, to train and predict from the incomplete and extremely imbalanced data. It is more effective than the state-of-art method, AMI-Net. First, we also implement embedding, multi-head attention and gated attention-based multi-instance pooling to capture the relations of symptoms themselves and with the given disease. Besides, we propose var-ious improvements to AMI-Net, that the cross-entropy loss is replaced by focal loss and we propose a novel self-adaptive multi-instance pooling method on instance-level to obtain the bag representation. We validate the performance of AMI-Net+ on two real-world datasets, from two different medical domains. Results show that our approach outperforms other base-line models by a considerable margin.



There are no comments yet.


page 9


Attention-based Multi-instance Neural Network for Medical Diagnosis from Incomplete and Low Quality Data

One way to extract patterns from clinical records is to consider each pa...

U-Net Based Multi-instance Video Object Segmentation

Multi-instance video object segmentation is to segment specific instance...

Multi-label Thoracic Disease Image Classification with Cross-Attention Networks

Automated disease classification of radiology images has been emerging a...

Dual-stream Maximum Self-attention Multi-instance Learning

Multi-instance learning (MIL) is a form of weakly supervised learning wh...

End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net

Information extraction from document images has received a lot of attent...

Inpatient2Vec: Medical Representation Learning for Inpatients

Representation learning (RL) plays an important role in extracting prope...

Attention-based Neural Bag-of-Features Learning for Sequence Data

In this paper, we propose 2D-Attention (2DA), a generic attention formul...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.