most citedSwitching Loss for Generalized Nucleus Detection in Histopathology

2 citations · 2 across the 2 of their papers we have counts for

collaborators

7 papers

eess.IV20202 cited

Switching Loss for Generalized Nucleus Detection in Histopathology

Deepak Anand, Gaurav Patel, Yaman Dang +1

The accuracy of deep learning methods for two foundational tasks in medical image analysis -- detection and segmentation -- can suffer from class imbalance. We propose a `switching…

cs.LG2020

Uncertainty Estimation in Cancer Survival Prediction

Hrushikesh Loya, Pranav Poduval, Deepak Anand +2

Survival models are used in various fields, such as the development of cancer treatment protocols. Although many statistical and machine learning models have been proposed to achie…

cs.CV2020

Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

Abhijeet Patil, Dipesh Tamboli, Swati Meena +2

Breast cancer has the highest mortality among cancers in women. Computer-aided pathology to analyze microscopic histopathology images for diagnosis with an increasing number of bre…

eess.IV2019

Pixel-wise Segmentation of Right Ventricle of Heart

Yaman Dang, Deepak Anand, Amit Sethi

One of the first steps in the diagnosis of most cardiac diseases, such as pulmonary hypertension, coronary heart disease is the segmentation of ventricles from cardiac magnetic res…

eess.IV2019

Histographs: Graphs in Histopathology

Shrey Gadiya, Deepak Anand, Amit Sethi

Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing can…

eess.SP2019

MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders

Kumar Yashashwi, Deepak Anand, Sibi Raj B Pillai +2

In this paper, we propose a low latency, robust and scalable neural net based decoder for convolutional and low-density parity-check (LPDC) coding schemes. The proposed decoders ar…