Deep learning classification of bacteria clones explained by persistence homology

Borowa, Adriana; Rymarczyk, Dawid; Ochońska, Dorota; Brzychczy-Włoch, Monika; Zieliński, Bartosz

doi:10.1109/ijcnn52387.2021.9533954

Cited by 4 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For various medical purposes, e.g. an epidemiological investigation [5] and an infection diagnosis [3], the classification of microbiological organisms, especially bacteria, is essential. Traditional methods of microorganism identification and classification are expensive and labour-intensive [7].…”

Section: Related Workmentioning

confidence: 99%

“…In the latest research, [5] used attention-based multiple instance learning pooling to classify clones of Klebsiella pneumoniae as well as persistence homology to obtain explanations of the model and description of each clone. Yu, et al of [32] created a hierarchical classification model for taxonomy purposes with PCA, LDA, and random forest, using gold nanoparticles measurements.…”

Section: Related Workmentioning

confidence: 99%

“…The interval value for standard deviation σ p was obtained experimentally using a training dataset to maximize the number of patches with clearly visible bacteria cells. Finally, following a good practice [49] and previous research [5], [28], [50], we normalize the remaining patches by subtracting the mean and dividing by the standard deviation. Both values are again derived from training patches.…”

Section: A Preprocessing Images and Extracting Patchesmentioning

confidence: 99%

“…Due to the long time required for the standard process of species identification and its high costs [2], it is beneficial to use methods that do not rely on conventional methods. Existing solutions can automatically distinguish between bacteria and fungi species [3], [4] or even bacteria clones [5] using microscopic images and deep learning methods. However, it is only possible for monoculture images, which requires multiple culture iterations.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

Borowa

Rymarczyk

Ochońska

et al. 2023

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

Preliminary microbiological diagnosis usually relies on microscopic examination and, due to the routine culture and bacteriological examination, lasts up to 11 days. Hence, many deep learning methods based on microscopic images were recently introduced to replace the time-consuming bacteriological examination. They shorten the diagnosis by 1-2 days but still require iterative culture to obtain monoculture samples. In this work, we present a feasibility study for further shortening the diagnosis time by analyzing polyculture images. It is possible with multi-MIL, a novel multi-label classification method based on multiple instance learning.To evaluate our approach, we introduce a dataset containing microscopic images for all combinations of four considered bacteria species. We obtain ROC AUC above 0.9, proving the feasibility of the method and opening the path for future experiments with a larger number of species.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Preprocessing Images and Extracting Patchesmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

Borowa

Rymarczyk

Ochońska

et al. 2023

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The dataset was annotated hence this technique can be classified as supervised learning. In [50], the authors differentiated between three strains of 'Klebsiella pneumonia' by using supervised learning'. The study [22] use a labelled dataset to detect bacterial vaginosis.…”

Section: Rq 12 Which Types Of Learning Have Been Applied?mentioning

confidence: 99%

A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Nagro¹

2022

IJACSA

View full text Add to dashboard Cite

Deep learning is an area of machine learning that has substantial potential in various fields of study such as image processing and computer vision. A large number of studies are published annually on deep learning techniques. The focus of this paper is on bacteria detection, identification, and classification. This paper presents a systematic literature review that synthesizes the evidence related to bacteria colony identification and detection published in the year 2021. The aim is to aggregate, analyse, and summarize the evidence related to deep learning detection, identification, and classification of bacteria and bacteria colonies. The significance is that the review will help experts and technicians to understand how deep learning techniques can apply in this regard and potentially further support more accurate detection of bacteria types. A total of 38 studies are analysed. The majority of the published studies focus on supervised-learningbased convolutional neural networks. Furthermore, a large number of studies make use of laboratory-prepared datasets as compared to open-source and industrial datasets. The results also indicate a lack of tools, which is a barrier in adapting academic research in industrial settings.

show abstract

Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset

Reddy

Kanabur

et al. 2023

2023 2nd International Conference on Computational Systems and Communication (ICCSC)

View full text Add to dashboard Cite

Deep learning classification of bacteria clones explained by persistence homology

Cited by 4 publications

References 44 publications

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset

Contact Info

Product

Resources

About