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AssigneeIdeas - Institute of Data Engineering, Analytics and Science Foundation
InventorsSUSHMITA MITRA, SUROCHITA PAL DAS, B. UMA SHANKAR
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A Convolutional Neural network (CNNs) based system and method based on the same for image analysis including for computer aided detection/diagnosis is provided, enabling an efficient and accurate delineation of diseased volumes in images/medical images. More particularly, the system of the present invention is adapted to incorporate deformable convolution (DC) module based processor means, along the down- and up-sampling pathways of the underlying U-Net architectural framework of said system for effective understanding and attending to deformable geometry of unknown transformations, considering deformable convolution (DC), unlike the basic convolution is advantageously not constrained by predefined geometrical structures of the kernels. Said system of the present invention advantageously also permits weighted combination of image feature components along the encoder and decoder arms of the deformable convolution (DC) processor module based system architectural network due to introduction of Weight Generation (WG) modules in said system, such that dynamic assignment of importance to relevant spatial locations of the corresponding image feature maps could be given so as to also boost overall accuracy while ensembling to enable reduction in module analytic error by simultaneously maintaining the generalization in performance in respect of robustness to noise to attain accurate and faster demarcation of the ROI (region of interest). Added to the aforesaid, preferred integration of Focal Asymmetric Similarity (F AS) loss function analytic based processor module/means in said system, allowed effective handling of class imbalance for further improved performance.
A Convolutional Neural network (CNNs) based system for image analysis including for computer aided detection/diagnosis comprising:
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