Attention-guided multi-scale deep object detection framework for lymphocyte analysis in IHC histological images. (14th October 2022)

• Record Type: Journal Article
• Authors: Rauf, Zunaira; Sohail, Anabia; Khan, Saddam Hussain; Khan, Asifullah; Gwak, Jeonghwan; Maqbool, Muhammad
• Is Part Of: Microscopy. Volume 72:Number 1(2023)
• Page Start: 27
• Page End: 42
• Publication Date: 2022-10-14
• Subjects: lymphocyte -- object detection -- attention-aware -- deep CNN -- backbone; Microscopy -- Periodicals; 502.825
• DOI: 10.1093/jmicro/dfac051
• Languages: English
• ISSNs: 2050-5698

Abstract

Abstract: Tumor-infiltrating lymphocytes are specialized lymphocytes that can detect and kill cancerous cells. Their detection poses many challenges due to significant morphological variations, overlapping occurrence, artifact regions and high-class resemblance between clustered areas and artifacts. In this regard, a Lymphocyte Analysis Framework based on Deep Convolutional neural network (DC-Lym-AF) is proposed to analyze lymphocytes in immunohistochemistry images. The proposed framework comprises (i) pre-processing, (ii) screening phase, (iii) localization phase and (iv) post-processing. In the screening phase, a custom convolutional neural network architecture (lymphocyte dilated network) is developed to screen lymphocytic regions by performing a patch-level classification. This proposed architecture uses dilated convolutions and shortcut connections to capture multi-level variations and ensure reference-based learning. In contrast, the localization phase utilizes an attention-guided multi-scale lymphocyte detector to detect lymphocytes. The proposed detector extracts refined and multi-scale features by exploiting dilated convolutions, attention mechanism and feature pyramid network (FPN) using its custom attention-aware backbone. The proposed DC-Lym-AF shows exemplary performance on the NuClick dataset compared with the existing detection models, with an F -score and precision of 0.84 and 0.83, respectively. We verified the generalizability of our proposed framework by participating in a publically open LYON'19 challenge. Results in terms of detection rate (0.76) and F -score (0.73) suggest that the proposed DC-Lym-AF can effectively detect lymphocytes in immunohistochemistry-stained images collected from different laboratories. In addition, its promising generalization on several datasets implies that it can be turned into a medical diagnostic tool to investigate various histopathological problems. Graphical Abstract