An attention guided convolutional neural network (CNN) for the classification of breast cancer histopathology images is proposed. Neural networks are generally applied as black box models and often the network’s decisions are difficult to interpret. Making the decision process transparent, and hence reliable is important for a computer-assisted diagnosis (CAD) system. Moreover, it is crucial that the network’s decision be based on histopathological features that are in agreement with a human expert. To this end, we propose to use additional region-level supervision for the classification of breast cancer histopathology images using CNN, where the regions of interest (RoI) are localized and used to guide the attention of the classification network simultaneously. The proposed supervised attention mechanism specifically activates neurons in diagnostically relevant regions while suppressing activations in irrelevant and noisy areas. The class activation maps generated by the proposed method correlate well with the expectations of an expert pathologist. Moreover, the proposed method surpasses the state-of-the-art on the BACH microscopy test dataset (part A) with a significant margin.

This paper presents a novel vision based approach for detecting rows of crop in paddy field. The precise detection of crop row enables a farm-tractor to autonomously navigate the field for successful inter-row weeding. While prior works on crop row detection rely primarily on various image based features, a deep neural network based approach for learning semantic graphics to directly extract the crop rows from an input image is used in this work. A deep convolutional encoder decoder network is trained to detect the crop lines using semantic graphics. The detected crop lines are then used to derive control signal for steering the tractor autonomously in the field. The results demonstrate that the proposed method is able to detect the rows of paddy accurately and enable the tractor to navigate autonomously along the crop rows even with a simple proportional only controller.

Nonlinear memristor-based neural network and a circuit-based learning system is addressed in this work. The weights of the neural network are based on the memristor bridge synapse and the learning architecture is designed in analog-digital mixed circuits by adopting a simple learning algorithm called random weight change algorithm. Though the memristor bridge can be efficiently used as a synapse, it still suffers from nonlinearity in weight programming at its extremes due to the boundary effect of memristors, which is a common phenomenon in most of the nano-devices. In this study, a novel architecture of a modified memristor bridge synapse is proposed that avoids the boundary effect issue by shifting the programming origin to the middle of the linear region. To demonstrate the effectiveness of the proposed method, multilayer neural network along with the end-to-end learning architecture is designed in circuit with nonlinear memristors and tested for several classical learning problems. Simulation results showing successful learning and correct behavior of replicated neural network are presented.

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into “trail” and “non-trail” categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.