7 papers
INTERACT-CMIL: Multi-Task Shared Learning and Inter-Task Consistency for Conjunctival Melanocytic Intraepithelial Lesion Grading
Mert Ikinci, Luna Toma, Karin U. Loeffler +6
Accurate grading of Conjunctival Melanocytic Intraepithelial Lesions (CMIL) is essential for treatment and melanoma prediction but remains difficult due to subtle morphological cue…
VeLU: Variance-enhanced Learning Unit for Deep Neural Networks
Ashkan Shakarami, Yousef Yeganeh, Azade Farshad +3
Activation functions play a critical role in deep neural networks by shaping gradient flow, optimization stability, and generalization. While ReLU remains widely used due to its si…
DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology
Yousef Yeganeh, Maximilian Frantzen, Michael Lee +3
While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost…
Stress-Aware Resilient Neural Training
Ashkan Shakarami, Yousef Yeganeh, Azade Farshad +3
This paper introduces Stress-Aware Learning, a resilient neural training paradigm in which deep neural networks dynamically adjust their optimization behavior - whether under stabl…
Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation
Ashkan Shakarami, Azade Farshad, Yousef Yeganeh +4
We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This a…
Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis
Yousef Yeganeh, Azade Farshad, Ioannis Charisiadis +5
Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models; however, such large datasets ar…