10 papers
SIMPLER: H&E-Informed Representation Learning for Structured Illumination Microscopy
Abu Zahid Bin Aziz, Syed Fahim Ahmed, Gnanesh Rasineni +8
Structured Illumination Microscopy (SIM) enables rapid, high-contrast optical sectioning of fresh tissue without staining or physical sectioning, making it promising for intraopera…
PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning
Syed Fahim Ahmed, Gnanesh Rasineni, Florian Koehler +7
Whole-slide image (WSI) classification in computational pathology is commonly formulated as slide-level Multiple Instance Learning (MIL) with a single global bag representation. Ho…
MedConcept: Unsupervised Concept Discovery for Interpretability in Medical VLMs
Md Rakibul Haque, KM Arefeen Sultan, Tushar Kataria +1
While medical Vision-Language models (VLMs) achieve strong performance on tasks such as tumor or organ segmentation and diagnosis prediction, their opaque latent representations li…
MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance
Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Elhabian
Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed l…
AC-MIL: Weakly Supervised Atrial LGE-MRI Quality Assessment via Adversarial Concept Disentanglement
K M Arefeen Sultan, Kaysen Hansen, Benjamin Orkild +7
High-quality Late Gadolinium Enhancement (LGE) MRI can be helpful for atrial fibrillation management, yet scan quality is frequently compromised by patient motion, irregular breath…
IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions
Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian
Hematoxylin and eosin (H&E)-stained slides are central to cancer diagnosis and monitoring, visualizing tissue architecture and cellular morphology. However, H&E lacks the molecular…