… In this paper, we proposed a multi-resolution multiple instance learning model that leverages
saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of …

… instance within each positive bag. Therefore, we attempt to incorporate multiple instance
learning into a deep learning … visual knowledge to assist the task of image classification. …

… In this paper, we proposed a new Multi-instance Learning (MIL) framework … Learning Systems
(BLS). Our HybridMIL can overcome several challenging issues over existing MIL methods …

… Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances … Multiple instance learning (MIL) deals with training data arranged in sets, called bags. …

… We follow a multiple-instance learning … multi-fold multiple instance learning procedure,
which prevents training from prematurely locking onto erroneous object locations. This procedure …

… these challenges by employing multiple instance learning (MIL) [9]. … multiple instance learning
(MIL). We presented a novel framework called multiple-segment multiple instance learning …

… framework uses multi-instance multi-label learning to build … Multi-instance learning (MIL)
differs from traditional learning … each training bag contains several instances of positive and …

… Gradient-based localization techniques do not produce good results on histopathology … In
this study, we use attention-based multiple instance learning [7] to produce better localization …

… Instead, we propose a method to predict these annotation spans without … a Multiple Instance
Learning (MIL) task. We show that such models can both predict content scores and localize …

… For the disproportion dataset used in this paper, we optimized a high precision localization
network and recalibrated multi-instance learning to solve the WSI classification problem. …