36 citations · 199 across the 37 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…
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…
M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning
Karol Gotkowski, Camila Gonzalez, Andreas Bucher +1
M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans. The attention maps can b…
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
David Kügler, Marc Uecker, Arjan Kuijper +1
Despite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical inst…
Deep Spectral Correspondence for Matching Disparate Image Pairs
Arun CS Kumar, Shefali Srivastava, Anirban Mukhopadhyay +1
A novel, non-learning-based, saliency-aware, shape-cognizant correspondence determination technique is proposed for matching image pairs that are significantly disparate in nature.…
GANs for Medical Image Analysis
Salome Kazeminia, Christoph Baur, Arjan Kuijper +4
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical…
How Bad is Good enough: Noisy annotations for instrument pose estimation
David Kügler, Anirban Mukhopadhyay
Though analysis of Medical Images by Deep Learning achieves unprecedented results across various applications, the effect of \emph{noisy training annotations} is rarely studied in…
Exploring Adversarial Examples: Patterns of One-Pixel Attacks
David Kügler, Alexander Distergoft, Arjan Kuijper +1
Failure cases of black-box deep learning, e.g. adversarial examples, might have severe consequences in healthcare. Yet such failures are mostly studied in the context of real-world…
i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery
David Kügler, Jannik Sehring, Andrei Stefanov +5
Purpose: Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. Current techniques lac…
Tool and Phase recognition using contextual CNN features
Manish Sahu, Anirban Mukhopadhyay, Angelika Szengel +1
A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here. In addition, me…
Morphological Analusis Of The Left Ventricular Eendocardial Surface Using A Bag-Of-Features Descriptor
Anirban Mukhopadhyay, Zhen Qian, Suchendra M. Bhandarkar +3
The limitations of conventional imaging techniques have hitherto precluded a thorough and formal investigation of the complex morphology of the left ventricular (LV) endocardial su…