Organoid-ICLIP: Class Imbalance-Aware Vision-Language Learning for Organoid Mitosis Classification

Visual analysis of brain organoid imaging is critical for advancing the understanding of in vitro organoid development. However, precise differentiation of mitotic stages remains challenging due to complex cellular morphology and limited imaging data. To address these limitations, we present Organoid-ICLIP, a novel Class Imbalance-Aware Contrastive Language-Image Pretraining model tailored for mitotic nuclei analysis. This approach integrates functional and morphological descriptions of mitotic phases by aligning organoid microscopy images with textual descriptions during visual pattern extraction. The proposed approach introduces a new Mitosis Phase Imbalance-Aware Balancing Loss (MPIA-BL), which dynamically adjusts instance weighting based on mitotic phase distribution to address severe class imbalance. By explicitly addressing both inter-class and intra-class imbalance, Organoid-ICLIP achieves more than 6% improvement over traditional contrastive learning approaches on evaluation metrics. Experimental evaluations on the benchmark Borg brain organoid imaging dataset demonstrate the enhanced performance of the proposed approach in mitotic phase classification compared to SOTA vision models.