Deep learning has demonstrated remarkable accuracy analyzing images for cancer detection tasks in recent years. The accuracy that has been achieved rivals radiologists and is suitable for implementation as a clinical tool. However, a significant problem is that these models are black-box algorithms therefore they are intrinsically unexplainable. This creates a barrier for clinical implementation due to lack of trust and transparency that is a characteristic of black box algorithms. Additionally, recent regulations prevent the implementation of unexplainable models in clinical settings which further demonstrates a need for explainability. To mitigate these concerns, there have been recent studies that attempt to overcome these issues by modifying deep learning architectures or providing after-the-fact explanations. A review of the deep learning explanation literature focused on cancer detection using MR images is presented here. The gap between what clinicians deem explainable and what current methods provide is discussed and future suggestions to close this gap are provided.

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In modern industrial manufacturing processes, robotic manipulators are routinely used in the assembly, packaging, and material handling operations. During production, changing end-of-arm tooling is frequently necessary for process flexibility and reuse of robotic resources. In conventional operation, a tool changer is sometimes employed to load and unload end-effectors, however, the robot must be manually taught to locate the tool changers by operators via a teach pendant. During tool change teaching, the operator takes considerable effort and time to align the master and tool side of the coupler by adjusting the motion speed of the robotic arm and observing the alignment from different viewpoints. In this paper, a custom robotic system, the NeXus, was programmed to locate and change tools automatically via an RGB-D camera. The NeXus was configured as a multi-robot system for multiple tasks including assembly, bonding, and 3D printing of sensor arrays, solar cells, and microrobot prototypes. Thus, different tools are employed by an industrial robotic arm to position grippers, printers, and other types of end-effectors in the workspace. To improve the precision and cycle-time of the robotic tool change, we mounted an eye-in-hand RGB-D camera and employed visual servoing to automate the tool change process. We then compared the teaching time of the tool location using this system and compared the cycle time with those of 6 human operators in the manual mode. We concluded that the tool location time in automated mode, on average, more than two times lower than the expert human operators.

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