Lakehead University Knowledge Commons

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Recent Submissions

Evaluating architecture scalability and transfer learning in urban scene segmentation using explainable AI
(MDPI, 2026-03-01) Hatkar, Tanmay Sunil; Pandey, Abhinav; Ahmed, Saad Bin
Semantic segmentation plays a pivotal role in autonomous driving, enabling pixel-level understanding of road scenes. Although transformer-based models such as SegFormer have shown exceptional performance on large datasets, their generalization to smaller and geographically diverse datasets remains underexplored. In this work, we analyze the scalability and transferability of SegFormer variants (B3, B4, B5) using CamVid as the base dataset. We perform cross-dataset transfer learning to KITTI and IDD, evaluate class-level performance, and explore explainable AI via confidence heatmaps. Our findings show that SegFormer-B5 achieves the highest accuracy (82.4% mIoU) on CamVid, while transfer learning from CamVid improves mIoU on KITTI by 2.57% and enhances class-specific predictions in IDD by over 70%. These results highlight the practical potential of SegFormer in real-world segmentation systems and the interpretability benefits of confidence-based visual analysis.
QuantFormer: A hybrid quantum classical transformer for hyperspectral image classification
(PMLR, 2026) Lunia, Jay Vinit; Ahmed, Saad Bin
Hyperspectral image (HSI) classification is challenging because each pixel has hundreds of spectral bands while only a small number of labelled samples are available. This paper presents QuantFormer, a hybrid quantum–classical transformer that embeds a small variational quantum circuit as a spectral token encoder inside a vision transformer backbone for pixel-wise land-cover mapping. A unified patch-based pipeline with band-wise normalization, principal component analysis, and quantum token encoding is evaluated on four benchmarks: Indian Pines, Pavia University, a 7-class Houston 2013 subset, and EuroSAT_MS. With roughly 35k trainable parameters, QuantFormer attains overall accuracy above 99% on the three airborne hyperspectral datasets and about 89.8% on EuroSAT_MS, competitive with deep 3D CNNs while using substantially fewer weights. Beyond full-data experiments, we also study limited-label regimes and provide practical guidance on when quantum token encoders are a viable alternative to classical projections, without claiming quantum advantage over the strongest classical baselines.
Incremental learning approach for semantic segmentation of skin histology images
(Springer Nature, 2026-03-06) Fatima, Sana; Salam, Anum Abdul; Akram, Muhammad Usman; Hameed, Ibrahim A.; Ahmed, Saad Bin
This study presents an incremental learning framework to enhance the generalization and robustness of transformer-based deep learning models for segmenting skin cancer and related tissue structures. While deep learning models often perform well on data distributions similar to their training sets, their accuracy typically degrades when exposed to novel scenarios–limiting their clinical utility in skin cancer diagnosis. To address this, we propose a biologically inspired incremental learning strategy tailored for skin cancer classification and segmentation, allowing the model to incorporate new data progressively while reducing catastrophic forgetting. Our approach integrates multiple loss functions to preserve existing knowledge while adapting to additional magnification levels. Experimental results on the indistribution test set demonstrate consistent performance improvements: achieving 89.05% accuracy with 10× magnification, 92.68% with 10× and 5× combined, and 95.53% when incorporating 10×, 5×, and 2× magnifications. These findings highlight the potential of our method to improve the adaptability and reliability of deep learning systems for empirical generalization in skin cancer classification tasks.
Laboratory- and field- scale bioassays for predicting responses of wild rice (Zizania palustris L.) to exposures of site-specific sediments and waters
(2020) Tedrow, O’Niell Roy; Lee, Peter; Leung, Kam; Kanavillil, Nandakumar; Bloom, Paul
Four bioassays were used to expose wild rice (WR) to site-specific waters and sediments within the boundaries of site-specific conditions. Mesocosm- and microcosm- scale bioassays were developed to measure responses of WR to sediment exposures. In mesocosms, WR height (HT), dry weight biomass (DWB), and seed production (SP) were statistically lower for Cleaver and Unnamed Lake sediment-grown plants compared to Rat River Bay sediment-grown plants. An accelerated-growth microcosm-scale bioassay accurately represented the mesocosm-scale bioassay, while decreasing overall time, sediment, water, and space. WR developed to near reproductive maturity. Significant differences were not identified between mesocosm:microcosm ratios for WR DWB or SP, which appeared to be primarily influenced by sediment ammonia-nitrogen concentrations. Rafts were deployed in two select aquatic systems: three in the Seine River (non-industry-influenced); and two rafts in each of three legacy-mine influenced pits to determine the life stage (aerial, floating leaf, submerged) more sensitive to water depth and water depth increases. Based on data obtained during this study, floating leaf plants were determined more sensitive to depth increases; aerial stage was least sensitive to depth increases. WR developed to aerial stage in 20 and 40 cm water depth treatments in all legacy-mine influenced pits indicating no adverse responses to pit waters. Two flow-through WR paddies were constructed adjacent to separate legacy-mine influenced pits with elevated sulphate concentrations. Seeded WR in each paddy developed according to typical phenology during each of multiple successive growing seasons. In the Pit A paddy, no statistical decreases in HT or DWB were observed between growing seasons; average SP statistically increased in 2019; and seed DWB remained statistically similar between growing seasons. Over two successive growing seasons, WR stem density remained statistically similar in the Pit C paddy. Despite conditions potentially conducive to iron sulphide root coating formation, this was not identified via SEM-EDX characterization. No adverse WR responses observed throughout this study were determined resultant of Pit A or Pit C water exposures. Representativeness of natural WR areas is paramount to bioassay data defensibility. Bioassays described herein were designed to represent field conditions to the extent possible given the scale of the bioassay.
The geologic setting, kinematics and deformation mechanisms of the Eagle River gold deposit, Northern Ontario
(2026) Barkley, Ryan J.; Hollings, Pete
The Neoarchean Eagle River orogenic gold deposit in the Mishibishu greenstone belt of northern Ontario, Canada, was investigated through a multidisciplinary approach integrating field mapping, lithogeochemistry, isotope analysis, EBSD mapping, CVA analysis and geothermometry techniques. Near mine host rocks comprise two distinct suites: tholeiitic and calc-alkaline volcanic rocks. When normalized to primitive mantle values, the tholeiitic basalts and gabbros have negative Nb and Ti anomalies, near flat LREE (La/Smpm = 0.94-1.25) to weakly fractionated HREE patterns (Gd/Ybpm = 0.88-1.62) and εNd T(2700) values of +1.93 to +2.50, indicating little crustal contamination and formation within a primitive intra-oceanic plateau setting analogous to the Phanerozoic Ontong Java plateaus. The second suite (calc-alkaline) comprises intermediate and felsic samples, including diorites, andesites, granodiorites, and granites. The intermediate samples exhibit negative Nb and Ti anomalies, with moderate LREE patterns (La/Smpm = 1.29-5.82) and HREE fractionation (Gd/Ybpm = 0.70-4.28). The felsic samples have moderate LREE enrichment (La/Smpm = 1.29-5.82), HREE fractionation (Gd/Ybpm = 0.70-4.28), and εNd(2700 Ma) values of +1.53 to +2.56, suggesting evolution in a closed system through subduction-driven fractional crystallization, similar to the modern-day western Canadian Cordillera orogeny. The Eagle River deformation zone is a ~4 km-long, curvilinear, along-strike brittle–ductile shear zone characterized by oblique dip-slip deformation. It records two dominant kinematic regimes: a pure shear–dominated component associated with oblique crustal shortening, and a simple shear component reflecting dextral reverse transpression. Sheared quartz veins have been fully recrystallized and exhibit non-coaxial deformation with triclinic flow geometry. Gold mineralization preferentially occurs at vein selvages where fractured plagioclase networks provide a brittle contrast with dynamically recrystallized quartz veins and veinlets. Sheared quartz grains show evidence of grain boundary migration within the dislocation creep regime, with gold grains, sulphides, and other impurities concentrating at the quartz vein selvage. These findings support a tectonic model in which subduction-related, calc-alkaline rocks were emplaced on primitive oceanic plateau crust in an intra-oceanic island arc environment and underwent ductile deformation, with gold concentration facilitated by crystal plastic deformation, creating favorable structural traps and fluid pathway arrays. This research enhances our understanding of the formation and tectonic framework of this high-grade gold deposit, providing insights into exploration prospectivity within similar geological settings.