Keratocytes are vital for maintaining the overall health of human cornea as they preserve the corneal transparency and help in healing corneal injuries. Manual segmentation of keratocytes is challenging, time consuming and also needs an expert. Here, we propose a novel semi-automatic segmentation framework, called Conditional Random Field Weakly Supervised Segmentation (CRF-WSS) to perform the ...

A general inductive Bayesian classification framework is introduced for data from multiple finite alphabets using predictive representations based on random urn models and generalized exchangeability. We develop a novel principle of generative supervised and semi-supervised probabilistic classification based on marginalizing simultaneous predictive classification probabilities for all test item...

The integration of ML and loT can provide insightful details for critical decision making, automated responses, etc. Predicting future trends detecting anomalies are some the areas where being used at a rapid rate. Machine learning help decode hidden patterns in IoT data. It may complement or replace manual processes with systems that use statistically derived behavior. In healthcare, wearable ...

Abstract Recently, graph contrastive learning (GCL) has achieved remarkable performance in representation learning. However, existing GCL methods usually follow a dual-channel encoder network (i.e., Siamese networks), which adds to the complexity of architecture. Additionally, these overly depend on varied data augmentation techniques, corrupting information. Furthermore, they are heavily relia...

Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simulta...

A new framework for adapting common ensemble clustering 9 methods to solve the image segmentation combination problem is pre10 sented. The framework is applied to the parameter selection problem in 11 image segmentation and compared with supervised parameter learning. 12 We quantitatively evaluate 9 ensemble clustering methods requiring a 13 known number of clusters and 4 with adaptive estimati...

ABSTRACT Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising e ective but increasingly speci c clustering algorithms. In contrast, we have developed a generalized framework that accommodates diverse clustering algorithms in a systematic way. This framework views clustering as a general process of iterative optimiza...

OBJECTIVE This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. MATERIALS AND METHODS The compar...