Search Results for author: Nishchal Sapkota

Found 7 papers, 2 papers with code

Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction

no code implementations18 Mar 2024 Hongxiao Wang, Yang Yang, Zhuo Zhao, Pengfei Gu, Nishchal Sapkota, Danny Z. Chen

For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, and genomic (e. g., bulk RNA-seq) for quantifying gene expressions.

Survival Prediction

SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification

1 code implementation6 Feb 2024 Nishchal Sapkota, Yejia Zhang, Sirui Li, Peixian Liang, Zhuo Zhao, Jingjing Zhang, Xiaomin Zha, Yiru Zhou, Yunxia Cao, Danny Z Chen

We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images.

Morphology classification

SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings

1 code implementation23 Jul 2023 Yejia Zhang, Pengfei Gu, Nishchal Sapkota, Danny Z. Chen

Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions.

3D Shape Reconstruction Image Segmentation +4

Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation

no code implementations15 Nov 2022 Yejia Zhang, Xinrong Hu, Nishchal Sapkota, Yiyu Shi, Danny Z. Chen

Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations.

Clustering Contrastive Learning +4

A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning

no code implementations15 Nov 2022 Yejia Zhang, Pengfei Gu, Nishchal Sapkota, Hao Zheng, Peixian Liang, Danny Z. Chen

High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance.

Image Segmentation Medical Image Segmentation +3

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