Restoring haptic feedback remains a major challenge in robot-assisted minimally invasive surgery (RMIS), especially for localizing subsurface tumors and estimating their depth. This paper presents a compact, high-density tactile sensor array based on fiber Bragg grating (FBG) sensing for robotic palpation. The array uses a honeycomb topology to increase spatial sampling within an 8.5 mm footprint while preserving high sensitivity. Static and dynamic tests show a linear force-wavelength response across seven channels, an average force resolution of 8.43 mN, and \((<)\)1% full-scale dynamic error. To estimate tumor depth from palpation signals, we propose FBG-PatchFormer. Since depth annotations are scarce and costly to obtain, FBG-PatchFormer leverages contrastive self-supervised pretraining on unlabeled palpation windows to reduce the reliance on dense labels, and is then finetuned for depth classification. On phantom palpation with embedded inclusions, FBG-PatchFormer achieves 99.60% record-level accuracy on a ten-class depth task (blank and 2--10 mm). In vivo tests on porcine liver further demonstrate robust tumor localization under physiological motion and fluid interference, supporting the clinical potential of the proposed sensing system.