36 citations · 199 across the 37 of their papers we have counts for
40 papers
Statistics of Moduli Space of vector bundles II
Arijit Dey, Sampa Dey, Anirban Mukhopadhyay
Let be a smooth irreducible projective curve of genus over a finite field $\F_{q}$ of characteristic with elements such that the function field $\F_{q}(X)$ i…
M3D-NCA: Robust 3D Segmentation with Built-in Quality Control
John Kalkhof, Anirban Mukhopadhyay
Medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures. However, the real-world utility of such models is limited by their…
Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning
Niklas Babendererde, Moritz Fuchs, Camila Gonzalez +2
Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catast…
Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models
Yannik Frisch, Moritz Fuchs, Antoine Sanner +7
Cataract surgery is a frequently performed procedure that demands automation and advanced assistance systems. However, gathering and annotating data for training such systems is re…
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
John Kalkhof, Camila González, Anirban Mukhopadhyay
Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentat…
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Xiangyu Li, Gongning Luo, Kuanquan Wang +31
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores…
Federated Stain Normalization for Computational Pathology
Nicolas Wagner, Moritz Fuchs, Yuri Tolkach +1
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology.…
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
Amin Ranem, John Kalkhof, Caner Özer +2
While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR)…
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
Camila Gonzalez, Amin Ranem, Ahmed Othman +1
Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explo…
FrOoDo: Framework for Out-of-Distribution Detection
Jonathan Stieber, Moritz Fuchs, Anirban Mukhopadhyay
FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, an…
Continual Hippocampus Segmentation with Transformers
Amin Ranem, Camila González, Anirban Mukhopadhyay
In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most e…
Improving robustness and calibration in ensembles with diversity regularization
Hendrik Alexander Mehrtens, Camila González, Anirban Mukhopadhyay
Calibration and uncertainty estimation are crucial topics in high-risk environments. We introduce a new diversity regularizer for classification tasks that uses out-of-distribution…
Disentanglement enables cross-domain Hippocampus Segmentation
John Kalkhof, Camila González, Anirban Mukhopadhyay
Limited amount of labelled training data are a common problem in medical imaging. This makes it difficult to train a well-generalised model and therefore often leads to failure in…
Quality monitoring of federated Covid-19 lesion segmentation
Camila Gonzalez, Christian Harder, Amin Ranem +5
Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fas…
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
Antoine Sanner, Camila Gonzalez, Anirban Mukhopadhyay
The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distr…
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
Marius Memmel, Camila Gonzalez, Anirban Mukhopadhyay
Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability. To still obtain viable models, continual learning aims to train in se…
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Andreas Bucher +3
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models a…
Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation
Manish Sahu, Anirban Mukhopadhyay, Stefan Zachow
Purpose: Segmentation of surgical instruments in endoscopic videos is essential for automated surgical scene understanding and process modeling. However, relying on fully supervise…
A survey on shape-constraint deep learning for medical image segmentation
Simon Bohlender, Ilkay Oksuz, Anirban Mukhopadhyay
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmenta…
Super-Selfish: Self-Supervised Learning on Images with PyTorch
Nicolas Wagner, Anirban Mukhopadhyay
Super-Selfish is an easy to use PyTorch framework for image-based self-supervised learning. Features can be learned with 13 algorithms that span from simple classification to more…
Understanding Interpretability by generalized distillation in Supervised Classification
Adit Agarwal, K. K. Shukla, Arjan Kuijper +1
The ability to interpret decisions taken by Machine Learning (ML) models is fundamental to encourage trust and reliability in different practical applications. Recent interpretatio…
What is Wrong with Continual Learning in Medical Image Segmentation?
Camila Gonzalez, Nick Lemke, Georgios Sakas +1
Continual learning protocols are attracting increasing attention from the medical imaging community. In continual environments, datasets acquired under different conditions arrive…
EXCLUVIS: A MATLAB GUI Software for Comparative Study of Clustering and Visualization of Gene Expression Data
Sudip Poddar, Anirban Mukhopadhyay
Clustering is a popular data mining technique that aims to partition an input space into multiple homogeneous regions. There exist several clustering algorithms in the literature.…
Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation
Manish Sahu, Ronja Strömsdörfer, Anirban Mukhopadhyay +1
Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised…