180 citations · 581 across the 31 of their papers we have counts for
9 papers · 1 filter
Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models
Nils Gessert, Marcel Bengs, Alexander Schlaefer
The initial assessment of skin lesions is typically based on dermoscopic images. As this is a difficult and time-consuming task, machine learning methods using dermoscopic images h…
Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data
Nils Gessert, Maximilian Nielsen, Mohsin Shaikh +2
In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified bas…
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels
Nils Gessert, Martin Gromniak, Marcel Bengs +2
Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be know…
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
Nils Gessert, Alexander Schlaefer
Cardiac left ventricle (LV) quantification provides a tool for diagnosing cardiac diseases. Automatic calculation of all relevant LV indices from cardiac MR images is an intricate…
Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion
Nils Gessert, Torben Priegnitz, Thore Saathoff +6
Purpose. Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and…
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images
Nils Gessert, Marcel Bengs, Lukas Wittig +4
Purpose: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, re…
Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting
Nils Gessert, Thilo Sentker, Frederic Madesta +5
Objective: This work addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectur…
Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation
Nils Gessert, Alexander Schlaefer
Typically, deep learning architectures are handcrafted for their respective learning problem. As an alternative, neural architecture search (NAS) has been proposed where the archit…
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
Nils Gessert, Matthias Schlüter, Sarah Latus +3
Early detection of cancer is crucial for treatment and overall patient survival. In the upper aerodigestive tract (UADT) the gold standard for identification of malignant tissue is…