By leveraging the power of advanced technologies like big data and artificial intelligence (AI), healthcare providers can not only improve diagnosis accuracy but also enable a proactive approach that allows for early intervention and personalized preventive measures, ultimately leading to better overall health management. Integration of diverse sources of medical data for example, patient feedback, genetic data, and electronic health records can provide a holistic understanding of each patient’s unique circumstances and also lead to the discovery of new patterns and correlations that may further enhance medical research and advancements in biotechnology.
In multi-modal technology, a single mode of the medical image can supplement the weakness of another mode to accurately evaluate the medical condition and obtain diagnostic information through the fusion of information from multiple modes. Multi-modal fusion is related to representations, and a process that focuses on using some architecture to merge representations of different single models is classified as fusion.
HSA Solutions to Medical Imaging
Medical imaging is an effective way for disease screening and lesion localisation, but mass data means the professional have to spend alot of time to analyze and draw conclusions that are mostly subjective. Use of Artificial Intelligence can not only speed up the procedure, but can reduce the chances of misdiagnosis. However there are some issues:
- Uneven Image quality: Some images are relatively poor in quality, with a lot of noise, blurred edges, and the background of the images is complex and inconsistent, thus it is very difficult to extract features and analyze them
- Lack of Standardisation: Large sets of data requires manual annotation by a professional, privacy protection of the patient, scarce and non-generalised data labelling
- Image inaccuracy: Medical images have multi-modal image channels with complex noise in contrast to conventional natural image segmentation. Real-time performance and higher image accuracy are necessary.
HS Analysis uses Deep Learning algorithms that are well suited to take advantage of the complex and heterogeneous data types obtained in modern clinical practice to learn the extremely complex relationship between features and labels, so as to assist doctors to make relevant analyses and predictions.
Using Mass Spectrometry data
Metabolic reprogramming during the earliest stages of cancer growth initiates a radical change in cell chemistry. Evidence of oncogenesis can be found in subtle changes to the composition of tumors as they progress from abnormal cells to aggressive metastatic cancers. Cancer research has increasingly embraced mass spectrometry-based profiling of clinical specimens (such as tissues and biofluids). A frequent strategy is to describe differences in protein expression between tumor and healthy tissue or between blood from sick and healthy people.
There are currently numerous forms of MSI techniques available, some prominent ones are:
- Liquid chromatography-mass spectrometry (LC-MS): utilized to obtain a global proteomic profile of a biological sample
- Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS): ICP-MS is an analytical technique widely used for quantitative element determination in liquid samples. Laser ablation is used for solid sample introduction to ICP-MS
- Probe electrospray ionization-mass spectrometry (PESI-MS): rapidly visualize mass spectra of small, surgically obtained tissue samples
(A) Surgical tissue resections analysed by LA-ICP-MS to produce quantitative maps of each measured element.
(B) Haematoxylin and eosin (H&E) stained sections annotated according to tissue type by a pathologist, which were then overlaid on LA-ICP-MS images and used to extract spatial element concentrations in four distinct anatomical features: primary tumour, adenomatous polyp, mucosa layers, and smooth muscle tissue
HSA KIT: a better way to access MS files
Modern high-throughput mass spectrometry (MS)-based proteomics experiments require data processing, management, and visualization, which are sometimes some of the most time-consuming procedures, especially for labs without considerable bioinformatics help. An growth in the creation of new software libraries, including freely accessible and open-source software, has been sparked by the growing interest in the subject of proteomics.
|OpenMS||C++ (+ Python binding)||customisable tools for Proteomics and Metablomics data|
|Basis||Python||analytical pre-processing workflow for raw MSI data, scalable processing|
|Cardinal||R||pre-processing, spatial segmentation, and classification of MS data|
|Trans-Proteomic Pipeline (TPP)||C++||MS data visualization, peptide identification and validation, protein identification, quantification, and annotation, data storage and mining|
|MatchMS||Python||Import, process, clean, and compare MS data|
Research is a time-consuming process that necessitates long-term answers. The use of such libraries restricts the ability to integrate with future ambitions. Furthermore, relying only on pre-existing libraries may limit one’s ability to adapt to and incorporate new technologies or approaches that arise in the future. It is critical for researchers and developers to be able to experiment with new methodologies and tailor their solutions to changing research needs.
HSA KIT offers seamless integration with existing laboratory equipment and software systems, streamlining the workflow and maximizing efficiency. This allows researchers and developers to focus more on their scientific goals rather than technical challenges.
A community of bioinformaticians and software developers develop and maintain a wide range of software solutions covering majority elements of MS data analysis.
Common MS data processing tasks include theoretical proteome analysis, raw spectral processing, file format conversions, identification statistic generation, and storage/visualization of raw data, identification, and quantitation results.
|Software name||Data format|
|OpneMSI||imzML, HDF5 database|
|DataCubeExplorer||imzML, Biomap, AMOLF Datacube|
|DIA-NN||raw, wiff, mzML, dia|
Open source software has certain drawbacks, such as limited compatibility with certain data formats, a difficult user interface, and a lack of thorough technical support.
In addition to streamlining the analysis process for mass spectrometry, HSA KIT offers an adaptable platform for incorporating deep learning techniques. This extends its potential beyond conventional mass spectrometry applications by enabling the modification of modules to suit a variety of applications like illness detection, food toxicity certification, or principal component analysis. Not to mention the capability to work with various data types and, if needed, carry out automatic file conversions.
Medical Image fusion
Multi-modal medical image fusion based on deep learning can be used to effectively extract and integrate the feature information of different modes, improve the clinical applicability of medical images in the diagnosis and evaluation of medical problems, and provide quantitative analysis, real-time monitoring, and treatment planning for doctors and researchers.
There are currently two main approaches to multi-modal tasks: light fusion and heavy fusion.
- Light fusion: usually effortless, such as the vector inner product, as represented by CLIP and ALIGN, which use a two-tower structure focusing on multi-modal alignment to facilitate text matching, retrieval, and other downstream tasks.
- Heavy fusion: based on pre-trained Transformers, as represented by OSCAR, UNITER, VINVL, etc. Heavy fusion can interpret VQA, captions, and other downstream tasks that require information fusion and understanding, which the ALIGN algorithm cannot perform.
Fusion methods can be divided into late and early fusion according to their different locations.
There are three methods for the fusion of text and image: simple operation-based, attention-based, and tensor-based approaches.
Simple Operation-based fusion: Fusion is based on integrating feature vectors from different modes in simple ways, such as vector splicing, vector weighted sum, and so on. This method still has a problem; there is not enough interaction between the two modes, and the coupling relationship is insufficient.
Attention-based fusion: the mechanism gives different parts of the image feature vector different weights according to the characteristics of the image and text features. This enables the model to extract effective features from multi-source data and leverage the advantages of the multi-modal fusion
Tensor-based fusion: Through the stepwise decomposition of the weight tensor, an efficient multi-modal fusion model can be achieved
*Note: This website will be updated in future