Kanghyun Kim

TL;DR: This work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network, to increase the speed and accuracy of automated image classification.

TL;DR: A deep neural network is used to effectively design optical patterns for specimen illumination that substantially improve upon the ability to infer fluorescence image information from unstained microscope images, using an illumination model within the DNN's first layers that is jointly optimized during network training.

TL;DR: In this paper , a scalable multi-camera array microscope (MCAM) was proposed to enable high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics.

TL;DR: In this article, a scalable multi-camera array microscope (MCAM) is proposed to enable high-resolution recording from multiple spatial scales simultaneously, ranging from cellular structures to large-group behavioral dynamics.

TL;DR: A reinforcement learning system that adaptively explores optimal patterns to illuminate specimens for immediate classification by synthesizing knowledge over multiple snapshots is presented, demonstrating a smarter way to physically capture task-specific information.