Advances in Kidney Biopsy Structural Assessment through Dense Instance Segmentation

The kidney biopsy is the gold standard for the diagnosis of kidney diseases. Lesion scores made by expert renal pathologists are semi-quantitative and suffer from high inter-observer variability. Automatically obtaining statistics per segmented anatomical object, therefore, can bring significant benefits in reducing labor and this inter-observer variability. Instance segmentation for a biopsy, however, has been a challenging problem due to (a) the on average large number (around 300 to 1000) of densely touching anatomical structures, (b) with multiple classes (at least 3) and (c) in different sizes and shapes. The currently used instance segmentation models cannot simultaneously deal with these challenges in an efficient yet generic manner. In this paper, we propose the first anchor-free instance segmentation model that combines diffusion models, transformer modules, and RCNNs (regional convolution neural networks). Our model is trained on just one NVIDIA GeForce RTX 3090 GPU, but can efficiently recognize more than 500 objects with 3 common anatomical object classes in renal biopsies, i.e., glomeruli, tubuli, and arteries. Our data set consisted of 303 patches extracted from 148 Jones' silver-stained renal whole slide images (WSIs), where 249 patches were used for training and 54 patches for evaluation. In addition, without adjustment or retraining, the model can directly transfer its domain to generate decent instance segmentation results from PAS-stained WSIs. Importantly, it outperforms other baseline models and reaches an AP 51.7% in detection as the new state-of-the-art.

Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning

Generalized zero shot learning (GZSL) is still a technical challenge of deep learning as it has to recognize both source and target classes without data from target classes. To preserve the semantic relation between source and target classes when only trained with data from source classes, we address the quantification of the knowledge transfer and semantic relation from an information-theoretic viewpoint. To this end, we follow the prototypical model and format the variables of concern as a probability vector. Leveraging on the proposed probability vector representation, the information measurement such as mutual information and entropy, can be effectively evaluated with simple closed forms. We discuss the choice of common embedding space and distance function when using the prototypical model. Then We propose three information-theoretic loss functions for deterministic GZSL model: a mutual information loss to bridge seen data and target classes; an uncertainty-aware entropy constraint loss to prevent overfitting when using seen data to learn the embedding of target classes; a semantic preserving cross entropy loss to preserve the semantic relation when mapping the semantic representations to the common space. Simulation shows that, as a deterministic model, our proposed method obtains state of the art results on GZSL benchmark datasets. We achieve 21%-64% improvements over the baseline model -- deep calibration network (DCN) and for the first time demonstrate a deterministic model can perform as well as generative ones. Moreover, our proposed model is compatible with generative models. Simulation studies show that by incorporating with f-CLSWGAN, we obtain comparable results compared with advanced generative models.