Durgesh Kumar Singh

• Singh, Durgesh Kumar; Boubekki, Ahcene; Jenssen, Robert & Kampffmeyer, Michael (2025). SuperCM: Improving semi-supervised learning and domain adaptation through differentiable clustering. Pattern Recognition. ISSN 0031-3203. 171, p. 112117–112117. doi: 10.1016/j.patcog.2025.112117. Full text in Research Archive
• Singh, Durgesh Kumar; Cao, Qing; Thomas, Sarina; Boubekki, Ahcène; Jenssen, Robert & Kampffmeyer, Michael (2025). WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 16165, p. 218–227. doi: 10.1007/978-3-032-06329-8_21. Full text in Research Archive Show summary

  Clinical guidelines recommend performing left ventricular (LV) linear measurements in B-mode echocardiographic images at the basal level—typically at the mitral valve leaflet tips—and aligned perpendicular to the LV long axis along a virtual scanline (SL). However, most automated methods estimate landmarks directly from B-mode images for the measurement task, where even small shifts in predicted points along the LV walls can lead to significant measurement errors, reducing their clinical reliability. A recent semi-automatic method, EnLVAM, addresses this limitation by constraining landmark prediction to a clinician-defined SL and training on generated Anatomical Motion Mode (AMM) images to predict LV landmarks along the same. To enable full automation, a contour-aware SL placement approach is proposed in this work, in which the LV contour is estimated using a weakly supervised B-mode landmark detector. SL placement is then performed by inferring the LV long axis and the basal level—mimicking clinical guidelines. Building on this foundation, we introduce WiseLVAM—a novel framework for fully automated yet manually adaptable framework for automatically placing the SL and then automatically performing the LV linear measurements in the AMM mode. WiseLVAM utilizes the structure-awareness from B-mode images and the motion-awareness from AMM mode to enhance robustness and accuracy with the potential to provide a practical solution for the routine clinical application. The source code is publicly available at https://github.com/SFI-Visual-Intelligence/wiselvam.git.

• Singh, Durgesh Kumar; Boubekki, Ahcene; Jenssen, Robert & Kampffmeyer, Michael (2023). Supercm: Revisiting Clustering for Semi-Supervised Learning. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. ISSN 1520-6149. doi: 10.1109/ICASSP49357.2023.10095856. Full text in Research Archive Show summary

  The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex training strategies to obtain the desired results. In this work, we instead propose a novel approach that explicitly incorporates the underlying clustering assumption in SSL through extending a recently proposed differentiable clustering module. Leveraging annotated data to guide the cluster centroids results in a simple end-to-end trainable deep SSL approach. We demonstrate that the proposed model improves the performance over the supervised-only baseline and show that our framework can be used in conjunction with other SSL methods to further boost their performance.

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• Singh, Durgesh Kumar; Thomas, Sarina; Cao, Qing; Kampffmeyer, Michael; Jenssen, Robert & Solberg, Anne H Schistad (2026). Semi-Supervised nnU-Net with Weak–Strong Consistency and Boundary Refinement for Mitral Valve Segmentation. doi: https:/papers.miccai.org/miccai-2026-sat/paper/MVAA_089.pdf. Full text in Research Archive Show summary

  Analysis of the mitral valve begins with delineating the valve in the image, a step that is usually performed manually and is therefore slow and observer-dependent. The MICCAI 2026 Mitral Valve Anatomy Analysis (MVAA) challenge defines three segmentation tasks based on cardiac computed tomography (CT), three-dimensional transesophageal echocardiography (3D TEE), and intraoperative surgical video. The unpaired images differ in dimensionality, appearance, and label formulation. Therefore, a separate model is trained for each task within a common self-configuring nnU-Net framework. Labeled images are supervised with soft Dice loss and cross-entropy. Since mitral valve structures are thin, these region losses are complemented by a scheduled boundary loss averaged over the available foreground classes. Expert labels are scarce for cardiac CT and surgical video, while many unlabeled images are available. These unlabeled images are used through FixMatch-style weak–strong consistency learning, where a weak view without intensity augmentation generates confidence-filtered pseudo-labels for intensity-augmented strong views. On the private test set held by the challenge organizers, the proposed approach achieves Dice similarity coefficients of 0.83 for cardiac CT, 0.81 for 3D TEE, and 0.55 for surgical video. Hausdorff distance and average surface distance are reported as boundary measures. The code is publicly available at https://github.com/Durgesh93/MVAA.git.

• Bjørklund, Petter; Singh, Durgesh Kumar; Steen, Erik Normann & Kampffmeyer, Michael (2025). Slik kan KI bidra til å oppdage hjertesykdommer raskere. uit.no. doi: https:/uit.no/nyheter/artikkel?p_document_id=913917. Full text in Research Archive
• Singh, Durgesh Kumar (2024). Benefits of Anatomical Motion Mode Imaging in LV Automatic Measurement. Full text in Research Archive

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