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Chapter and Conference Paper
Multi-task Learning to Improve Semantic Segmentation of CBCT Scans using Image Reconstruction
Semantic segmentation is a crucial task in medical image processing, essential for segmenting organs or lesions such as tumors. In this study we aim to improve automated segmentation in CBCTs through multi-tas...
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Chapter and Conference Paper
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis
Multiple instance learning is a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations. In spite of the huge size of whole slide images, the number of indiv...
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Article
Open AccessAutomated major psoas muscle volumetry in computed tomography using machine learning algorithms
The psoas major muscle (PMM) volume serves as an opportunistic imaging marker in cross-sectional imaging datasets for various clinical applications. Since manual segmentation is time consuming, two different a...
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Chapter and Conference Paper
Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure
Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this upscaling process, an IT service provider ...
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Chapter and Conference Paper
Anomaly Detection in Medical Imaging - A Mini Review
The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine...
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Chapter and Conference Paper
Correction to: Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks
In an older version of this paper, there was an error in the affiliation of the author Sebastien Couillard-Despres. This has been corrected.
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Chapter and Conference Paper
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks
In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-...
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Chapter and Conference Paper
Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities
Novel imaging technologies raise many questions concerning the adaptation of computer-aided decision support systems. Classification models either need to be adapted or even newly trained from scratch to explo...
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Chapter and Conference Paper
GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy
We introduce the idea of ‘image enrichment’ whereby the information content of images is increased in order to enhance segmentation accuracy. Unlike in data augmentation, the focus is not on increasing the num...
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Chapter and Conference Paper
Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study
Approaches relying on adversarial networks facilitate image-to-image-translation based on unpaired training and thereby open new possibilities for special tasks in image analysis
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Chapter and Conference Paper
Gradual Domain Adaptation for Segmenting Whole Slide Images Showing Pathological Variability
Although there is a strong demand, the utilization of automated segmentation approaches in histopathological imaging is often inhibited by a high degree of variability. To tackle the thereby arising challenges...
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Chapter and Conference Paper
Which Way Round? A Study on the Performance of Stain-Translation for Segmenting Arbitrarily Dyed Histological Images
Image-to-image translation based on convolutional neural networks recently gained popularity. Especially approaches relying on generative adversarial networks facilitating unpaired training open new opportunit...
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Chapter and Conference Paper
A Quantitative Assessment of Image Normalization for Classifying Histopathological Tissue of the Kidney
The advancing pervasion of digital pathology in research and clinical practice results in a strong need for image analysis techniques in the field of histopathology. Due to diverse reasons, histopathological i...
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Chapter and Conference Paper
Fully-Automated CNN-Based Computer Aided Celiac Disease Diagnosis
While a significant amount of research has been on computer aided diagnosis of celiac disease, challenges remain especially due to difficult imaging conditions during endoscopy which frequently result in image...
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Chapter and Conference Paper
Evaluation of i-Scan Virtual Chromoendoscopy and Traditional Chromoendoscopy for the Automated Diagnosis of Colonic Polyps
Image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy were reported to facilitate the detection and diagnosis of colonic polyps during endoscopic sessions. Here, we investigate th...
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Article
Open AccessMaking texture descriptors invariant to blur
Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature extraction methods in real-world applications. In this paper, focus is on making arbitrary...
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Chapter and Conference Paper
Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images
Histopathological whole slide images of the same organ stained with the same dye exhibit substantial inter-slide variation due to the manual preparation and staining process as well as due to inter-individual ...
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Chapter and Conference Paper
Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection
Approaches for detecting regions of interest in biomedical image data mostly assume that a large amount of annotated training data is available. Certainly, for unchanging problem definitions, the acquisition o...
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Chapter and Conference Paper
Dealing with Intra-Class and Intra-Image Variations in Automatic Celiac Disease Diagnosis
Computer aided celiac disease diagnosis is based on endoscopic images showing the villi structure in regions of the small bowel. Especially unavoidably variable illuminations and varying viewing angles of the ...
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Chapter and Conference Paper
Boosting Small-Data Performance of LBP: A Case Study in Celiac Disease Diagnosis
A major issue in computer aided celiac disease diagnosis is the prevalence of substantial intra-class and even intra-image variations. A method which splits the images into a set of smaller ones and finally ap...
