arXiv subjects

Surabhi Jain

Publications and source records attributed to Surabhi Jain.

3 recordsLinked to original sources

arXiv2026

A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories

We present a multi center breast fine needle aspiration cytology (FNAC) dataset designed for patch wise classification using C1 to C5 reporting labels. The prospective dataset includes 321 patients and 470 whole-slide images (WSIs) collected from participating tertiary medical centers in India between May 2023 and March 2026. Slides were stained using Papanicolaou (190 WSIs) or MayGrunwald Giemsa (280 WSIs), scanned on a Hamamatsu NanoZoomer S360 at 40X magnification and 0.25 microns per pixel, and stored directly in NDPI format. Across the 470 WSIs, 446 WSIs contain annotated patch regions, yielding 7,398 PNG image patches with expert-verified C1 to C5 labels. The release includes NDPI WSIs, WSI-level GeoJSON annotation files, extracted patch images, deidentified metadata, a data dictionary, a validation summary, a manifest linking WSIs to Zenodo records, and code for dataset inspection and reuse. The complete dataset is approximately 950 GB and is available through Zenodo.

cs.CV↗

arXiv2013

Parallel Search with Extended Fibonacci Primitive

Search pattern experienced by the processor to search an element in secondary storage devices follows a random sequence. Formally, it is a random walk and its modeling is crucial in studying performance metrics like memory access time. In this paper, we first model the random walk using extended Fibonacci series. Our simulation is done on a parallel computing model (PRAM) with EREW strategy. Three search primitives are proposed under parallel computing model and each primitive is thoroughly tested on an array of size $10^7$ with the size of random walk being $10^4$. Our findings reveal that search primitive with pointer jumping is better than the other two primitives. Our key contribution lies in modeling random walk as an extended Fibonacci series generator and simulating the same with various search primitives.

cs.DC↗

arXiv2013

Simpler Sequential and Parallel Biconnectivity Augmentation

For a connected graph, a vertex separator is a set of vertices whose removal creates at least two components and a minimum vertex separator is a vertex separator of least cardinality. The vertex connectivity refers to the size of a minimum vertex separator. For a connected graph $G$ with vertex connectivity $k (k \geq 1)$, the connectivity augmentation refers to a set $S$ of edges whose augmentation to $G$ increases its vertex connectivity by one. A minimum connectivity augmentation of $G$ is the one in which $S$ is minimum. In this paper, we focus our attention on connectivity augmentation of trees. Towards this end, we present a new sequential algorithm for biconnectivity augmentation in trees by simplifying the algorithm reported in \cite{nsn}. The simplicity is achieved with the help of edge contraction tool. This tool helps us in getting a recursive subproblem preserving all connectivity information. Subsequently, we present a parallel algorithm to obtain a minimum connectivity augmentation set in trees. Our parallel algorithm essentially follows the overall structure of sequential algorithm. Our implementation is based on CREW PRAM model with $O(Δ)$ processors, where $Δ$ refers to the maximum degree of a tree. We also show that our parallel algorithm is optimal whose processor-time product is O(n) where $n$ is the number of vertices of a tree, which is an improvement over the parallel algorithm reported in \cite{hsu}.

cs.DS↗