The Utility of Unsupervised Machine Learning in Anatomic Pathology.

Developing accurate supervised machine learning algorithms is hampered by the lack of representative annotated datasets. Most data in anatomic pathology are unlabeled and creating large, annotated datasets is a time consuming and laborious process. Unsupervised learning, which does not require annotated data, possesses the potential to assist with this challenge. This review aims to introduce the concept of unsupervised learning and illustrate how clustering, generative adversarial networks (GANs) and autoencoders have the potential to address the lack of annotated data in anatomic pathology. A review of unsupervised learning with examples from the literature was carried out. Clustering can be used as part of semisupervised learning where labels are propagated from a subset of annotated data points to remaining unlabeled data points in a dataset. GANs may assist by generating large amounts of synthetic data and performing color normalization. Autoencoders allow training of a network on a large, unlabeled dataset and transferring learned representations to a classifier using a smaller, labeled subset (unsupervised pretraining). Unsupervised machine learning techniques such as clustering, GANs, and autoencoders, used individually or in combination, may help address the lack of annotated data in pathology and improve the process of developing supervised learning models.

McAlpine ED ，Michelow P ，Celik T 《-》

Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods.

Qi GJ ，Luo J 《-》

Unsupervised and self-supervised deep learning approaches for biomedical text mining.

Biomedical scientific literature is growing at a very rapid pace, which makes increasingly difficult for human experts to spot the most relevant results hidden in the papers. Automatized information extraction tools based on text mining techniques are therefore needed to assist them in this task. In the last few years, deep neural networks-based techniques have significantly contributed to advance the state-of-the-art in this research area. Although the contribution to this progress made by supervised methods is relatively well-known, this is less so for other kinds of learning, namely unsupervised and self-supervised learning. Unsupervised learning is a kind of learning that does not require the cost of creating labels, which is very useful in the exploratory stages of a biomedical study where agile techniques are needed to rapidly explore many paths. In particular, clustering techniques applied to biomedical text mining allow to gather large sets of documents into more manageable groups. Deep learning techniques have allowed to produce new clustering-friendly representations of the data. On the other hand, self-supervised learning is a kind of supervised learning where the labels do not have to be manually created by humans, but are automatically derived from relations found in the input texts. In combination with innovative network architectures (e.g. transformer-based architectures), self-supervised techniques have allowed to design increasingly effective vector-based word representations (word embeddings). We show in this survey how word representations obtained in this way have proven to successfully interact with common supervised modules (e.g. classification networks) to whose performance they greatly contribute.

Nadif M ，Role F 《-》

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Kasieczka G ，Nachman B ，Shih D ，Amram O ，Andreassen A ，Benkendorfer K ，Bortolato B ，Brooijmans G ，Canelli F ，Collins JH ，Dai B ，De Freitas FF ，Dillon BM ，Dinu IM ，Dong Z ，Donini J ，Duarte J ，Faroughy DA ，Gonski J ，Harris P ，Kahn A ，Kamenik JF ，Khosa CK ，Komiske P ，Le Pottier L ，Martín-Ramiro P ，Matevc A ，Metodiev E ，Mikuni V ，Murphy CW ，Ochoa I ，Park SE ，Pierini M ，Rankin D ，Sanz V ，Sarda N ，Seljak U ，Smolkovic A ，Stein G ，Suarez CM ，Szewc M ，Thaler J ，Tsan S ，Udrescu SM ，Vaslin L ，Vlimant JR ，Williams D ，Yunus M ... -  《-》

CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images.

This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of labeled images, we can collect a larger number of unlabeled images. To utilize these unlabeled images, we have developed a Convolutional AutoEncoder based Semi-supervised Network (CAESNet) for improving the classification performance. We applied our method to an OE dataset collected from patients undergoing endoscope-based confocal laser endomicroscopy procedures for Barrett's esophagus at Emory Hospital, which consists of 429 labeled images and 2826 unlabeled images. Our CAESNet consists of an encoder with 5 convolutional layers, a decoder with 5 transposed convolutional layers, and a classification network with 2 fully connected layers and a softmax layer. In the unsupervised stage, we first update the encoder and decoder with both labeled and unlabeled images to learn an efficient feature representation. In the supervised stage, we further update the encoder and the classification network with only labeled images for multiclass classification of the OE images. Our proposed semisupervised method CAESNet achieves the best average performance for multiclass classification of OE images, which surpasses the performance of supervised methods including standard convolutional networks and convolutional autoencoder network. Our semisupervised CAESNet can efficiently utilize the unlabeled OE images, which improves the diagnosis and decision making for patients with Barrett's esophagus.

Tong L ，Wu H ，Wang MD 《-》