Identifying anatomical landmarks in endoscopic video frames is essential for the early diagnosis of gastrointestinal diseases. However, this task remains challenging due to variability in visual characteristics across different regions and the limite...
Boxing punch type classification and kinematic analysis are essential for coaches and athletes, providing critical insights into punch variety and effectiveness, which are vital for performance improvement. Existing methods for punch recognition and ...
Segmentation and measurement of cardiac chambers from ultrasound is critical, but laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same problematic manual annotations. We build a pipeline for self-s...
Astrocytes regulate synaptic activity across large brain territories via their complex, interconnected morphology. Emerging evidence supports the involvement of astrocytes in shaping relapse to opioid use through morphological rearrangements in the n...
Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervise...
Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality of life. They cause nasal congestion, facial pain, headaches, thick nasal discharge, and a reduced sense of smell. However, accurately diagnosing ...
Self-supervised learning (SSL) is a potent method for leveraging unlabelled data. Nonetheless, EEG signals, characterised by their low signal-to-noise ratio and high-frequency attributes, often do not surpass fully-supervised techniques in cross-subj...
BMC medical informatics and decision making
40312368
BACKGROUND AND OBJECTIVE: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning models that predict the risk of co...
The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, ...
BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, placing a significant burden on healthcare systems globally. Developing high-precision automated diagnostics requires large annotated datasets, which are costly and difficult to o...