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Found 8 papers, 0 papers with code
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
Needle positioning is essential for various medical applications such as epidural anaesthesia.
Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus
We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS.
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples.
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus
Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability.
Self-Supervised U-Net for Segmenting Flat and Sessile Polyps
This is followed by a supervised training on the limited Kvasir-Sessile dataset.
Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification
We analyze the value of using multiple hyperspectral bands compared to conventional RGB images and we study several machine learning models' ability to make use of the additional spectral information.
Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
A recent study has shown that hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors, as precancerous or cancerous lesions show specific spectral signatures that distinguish them from healthy tissue.
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
As the results were promising, automatic classification of lesions might be feasible which could assist experts in their decision making.
