5 papers
3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy
Amirhossein Kardoost, Lion Gleiter, Tingying Peng +1
Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells. We present a systematic comparison of…
HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy
Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula +10
Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups. In biological cell images, current self-supervised lea…
Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning
Muhammed Furkan Dasdelen, Fatih Ozlugedik, Anastasia Litinetskaya +3
Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities. We introduce a statistically grounded patient…
QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging
Luca Zedda, Davide Antonio Mura, Cecilia Di Ruberto +4
Attention-based Multiple Instance Learning aggregators in medical imaging are prone to attention concentration, producing overconfident and unstable predictions. We introduce QG-MI…
Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios
Salome Kazeminia, Carsten Marr, Bastian Rieck
Multiple instance learning (MIL) is a framework for weakly supervised classification, where labels are assigned to sets of instances, i.e., bags, rather than to individual data poi…