Leveraging Other Datasets for Medical Imaging Classification: Evaluation of Transfer, Multi-task and Semi-supervised Learning

This work evaluates and compares different supervised learning algorithms using a costsensitive approach to find a model that classifies legal rules related to pesticides as prohibitions and permissions. The naive Bayes classifier achieves the best results and it would be applicable because it doesn't misclassify prohibitions as permissions.

Download Full-text

Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/465 ◽

2021 ◽

Author(s):

Weijia Zhang

Keyword(s):

Medical Imaging ◽

Supervised Learning ◽

Real World ◽

State Of The Art ◽

Experimental Results ◽

Weakly Supervised Learning ◽

Variational Autoencoder ◽

Label Prediction ◽

Weakly Supervised ◽

Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

Download Full-text

Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.638182 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ruixin Yang ◽

Yingyan Yu

Keyword(s):

Neural Network ◽

Medical Imaging ◽

Object Detection ◽

Convolutional Neural Network ◽

Imaging Study ◽

Semantic Segmentation ◽

Vast Number ◽

Digital Medicine ◽

Extension Algorithms ◽

Imaging Classification

In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.

Download Full-text