AI in Healthcare

recent publications in AI in Healthcare:

1. MIDL

   Learning Retinal Representations from Multi-modal Imaging via Contrastive Pre-training

   Sükei, E., Rumetshofer, E., Schmidinger, N., Schmidt-Erfurth, U., Klambauer, G., and Bogunović, H.

   In Medical Imaging with Deep Learning, short paper track 2023

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   Contrastive representation learning techniques trained on large multi-modal datasets, such as CLIP and CLOOB, have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In the field of ophthalmology, large multi-modal datasets are conveniently accessible as retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography to evaluate the disease. Motivated by this, we propose a CLIP/CLOOB objective-based model to learn joint representations of the two retinal imaging modalities. We evaluate our modelś capability to accurately retrieve the appropriate OCT based on a fundus image belonging to the same eye. Furthermore, we showcase the transferability of the obtained representations by conducting linear probing and fine-tuning on several prediction tasks from OCT.

2. medRxiv

   Machine Learning based COVID-19 Diagnosis from Blood Tests with Robustness to Domain Shifts

   medRxiv preprint doi:10.1101/2021.04.06.21254997 2021

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   We investigate machine learning models that identify COVID-19 positive patients and estimate the mortality risk based on routinely acquired blood tests in a hospital setting. However, during pandemics or new outbreaks, disease and testing characteristics change, thus we face domain shifts. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (taking samples, laboratory), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. To countermand this effect, we propose methods that first identify domain shifts and then reverse their negative effects on the model performance. Frequent re-training and reassessment, as well as stronger weighting of more recent samples, keeps model performance and credibility at a high level over time. Our diagnosis models are constructed and tested on large-scale data sets, steadily adapt to observed domain shifts, and maintain high ROC AUC values along pandemics.

3. arXiv

   Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks

   Hofmarcher, M., Mayr, A., Rumetshofer, E., Ruch, P., Renz, P., Schimunek, J., Seidl, P., Vall, A., Widrich, M., Hochreiter, S., and Klambauer, G.

   2020

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   Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.

4. Detecting Cutaneous Basal Cell Carcinomas in Ultra-High Resolution and Weakly Labelled Histopathological Images

   Kimeswenger, S., Rumetshofer, E., Hofmarcher, M., Tschandl, P., Kittler, H., Hochreiter, S., Hötzenecker, W., and Klambauer, G.

   2019

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   Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving histological diagnosis by providing diagnosis suggestions, i.e. computer-assisted diagnoses is actively researched to improve safety, quality and efficiency. Increasingly, machine learning methods are applied due to their superior performance. However, typical images obtained by scanning histological sections often have a resolution that is prohibitive for processing with current state-of-the-art neural networks. Furthermore, the data pose a problem of weak labels, since only a tiny fraction of the image is indicative of the disease class, whereas a large fraction of the image is highly similar to the non-disease class. The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images. We demonstrate that attention-based models can indeed yield almost perfect classification performance with an AUC of 0.99.

5. ICLR

   Human-Level Protein Localization with Convolutional Neural Networks

   Rumetshofer, E., Hofmarcher, M., Röhrl, C., Hochreiter, S., and Klambauer, G.

   In 2018

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   Localizing a specific protein in a human cell is essential for understanding cellular functions and biological processes of underlying diseases. A promising, low-cost,and time-efficient...