6 papers
Dataset creation for supervised deep learning-based analysis of microscopic images -- review of important considerations and recommendations
Christof A. Bertram, Viktoria Weiss, Jonas Ammeling +6
Supervised deep learning (DL) receives great interest for automated analysis of microscopic images with an increasing body of literature supporting its potential. The development a…
SWAN -- Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces
Sweta Banerjee, Timo Gosch, Sara Hester +11
The annotation of large scale histopathology image datasets remains a major bottleneck in developing robust deep learning models for clinically relevant tasks, such as mitotic figu…
Effortless Vision-Language Model Specialization in Histopathology without Annotation
Jingna Qiu, Nishanth Jain, Jonas Ammeling +2
Recent advances in Vision-Language Models (VLMs) in histopathology, such as CONCH and QuiltNet, have demonstrated impressive zero-shot classification capabilities across various ta…
Benchmarking Foundation Models for Mitotic Figure Classification
Jonas Ammeling, Jonathan Ganz, Emely Rosbach +4
The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled imag…
Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
Christof A. Bertram, Viktoria Weiss, Taryn A. Donovan +6
Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has be…
Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification
Jonathan Ganz, Jonas Ammeling, Emely Rosbach +4
Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key ide…