Konstantinos M. Dafnis works at Rutgers University.


Improving Test-Time Adaptation For Histopathology Image Segmentation: Gradient-To-Parameter Ratio Guided Feature Alignment

In the field of histopathology, computer-aided systems face significant challenges due to diverse domain shifts. They include variations in tissue source organ, preparation and scanningprotocols. These domain shifts can significantly impact algorithms’ performance in histopathology tasks, such as cancer segmentation. In this paper, we address this problem byproposing a new multi-task extension of test-time adaptation (TTA) for simultaneous semantic, and instance segmentation of nuclei. First, to mitigate domain shifts during testing, weuse a feature alignment TTA method, through which we adapt the feature vectors of the target data based on the feature vectors’ statistics derived from the source data. Second, the ratioof Gradient norm to Parameter norm (G2P) is proposed to guide the feature alignment procedure. Our approach requires a pre-trained model on the source data, without requiringaccess to the source dataset during TTA. This is particularly crucial in medical applications where access to training data may be restricted due to privacy concerns or patient consent. Through experimental validation, we demonstrate that the proposed method consistently yields competitive results when applied to out-of-distribution data across multiple datasets.

OmniLabel: A Challenging Benchmark for Language-Based Object Detection

Language-based object detection is a promising direction towards building a natural interface to describe objects in images that goes far beyond plain category names. While recent methods show great progress in that direction, proper evaluation is lacking. With OmniLabel, we propose a novel task definition, dataset, and evaluation metric. The task subsumes standard and open-vocabulary detection as well as referring expressions. With more than 30K unique object descriptions on over 25K images, OmniLabel provides a challenge benchmark with diverse and complex object descriptions in a naturally open-vocabulary setting. Moreover, a key differentiation to existing benchmarks is that our object descriptions can refer to one, multiple or even no object, hence, providing negative examples in free-form text. The proposed evaluation handles the large label space and judges performance via a modified average precision metric, which we validate by evaluating strong language-based baselines. OmniLabel indeed provides a challenging test bed for future research on language-based detection.